Space-time calibration system and method

ABSTRACT

A space-time calibration system and method implement space-time solutions, in which, in one preferred embodiment, a single node determines its own space-time solutions based on other network nodes with which the single node communicates. In other preferred embodiments, space-time solutions for the node can be generated using other resources in the network. The disclosed system and method enable reliable, precise object positioning particularly for environments where the Global Positioning System (GPS) is blocked or subject to interference such as within the urban core.

TECHNICAL FIELD

This disclosure relates to embodiments of a “PhaseNet” technique, whichis shorthand for implementation of space-time solutions, in which, inone preferred embodiment, a single node determines its own space-timesolutions based on other network nodes with which the single nodecommunicates. In other preferred embodiments, space-time solutions forthe node can be generated using other resources in the network. PhaseNetenables reliable, precise object positioning particularly forenvironments where the Global Positioning System (GPS) is blocked orsubject to interference such as within the urban core.

BACKGROUND INFORMATION

Current techniques for positioning within the urban environment includethe following:

-   -   Cellular:        -   GPS, A-GPS, Time of Arrival, Radio Fingerprinting, Cell            Global Identity, and Enhanced Cell ID.    -   WiFi:        -   Radio Fingerprinting, WiFi TOA, and Wireless Access Point            Wardriving.    -   GPS:        -   DGPS, WAAS, A-GPS, and Laser Augmentation.

GPS is a space-based satellite constellation that provides constant,global geopositioning to the end user. A user's GPS device processessignals from three or more GPS satellites, and by way of trilaterationprovides latitude and longitude data. GPS may be global, but it is notubiquitous because foliage, building edifices, and bridges all block GPSsignals. The absence of GPS signals in places such as the urban core isproblematic because high value applications for reliable positioningabound, yet no reliable solution exists. The urban core in major citiesis also known as the “urban canyon,” because skyscrapers on both sidesof the streets block GPS signals. Another common problem is multipathdistortion in which signals bounce off many surfaces before reaching theuser, resulting in vastly inaccurate positioning data.

PhaseNet is original equipment manufacturer (OEM) “plumbing level”technology that is integrated into device and infrastructure productssuch as those sold by Motorola, Cisco, D-Link, Nokia, Ericsson, HP,Agilent, and Intel. This OEM technology is primarily in the form ofsoftware modules, but it also includes custom hardware for Military“MILSPEC” and custom applications. Additionally, hardware referencedesigns for very minor chip level modifications (IP) at the transceiverlevel can improve the performance. Because of its interoperability,PhaseNet can include any number of the additional positioning methodslisted above; however, PhaseNet can reside independent of these andstill provide sub-meter positioning to the end user.

To overcome the limitations of the GPS system mentioned above, a GPSextension can be provided by PhaseNet. Furthermore, PhaseNet can createan entirely independent navigational capability by embedding positioningdata into wireless networks that abound in the urban canyon. PhaseNet isan interoperable architecture; hence, it implements a capability toeffectively coordinate disparate devices operating at differentfrequencies, and with different standards and protocols. Thisarchitecture is reliable, precise, scalable, dynamic, broad baseline,plug-and-play, secure and cost-effective.

Potential users include city visitors relying on in-car navigationsystems that show onscreen location data provided by GPS. GPS fails whena traveler crosses the threshold into the urban canyon where unreliablesignals show inaccurate navigation data or may be blocked entirely.However, urban drivers are well within reception of reliable WiFi, WiMaxor cellular networks and PhaseNet positioning data embedded in thecorresponding wireless signals provide the necessary positioning data.

First responders tending to an emergency on a city sidewalk, copiertechnicians needing proof of performance for 50 copiers on variousfloors of a downtown building, courier dispatch wanting to know thelocation of a the driver, and city workers ensuring a backhoe is notcutting gas or fiber optic lines are all examples of situations in whichPhaseNet service can substitute for inadequate GPS capability.

The abundance of WiFi in urban settings provides an excellent example ofhow PhaseNet may leverage a robust and abundant signal source. Forexample, increasing deployments of individual WiFi access points bybusinesses and consumers make it difficult to walk through a downtownarea without detecting WiFi signals. WiFi “sniffers” typically installedon personal computer (PC) operating systems constantly look out for WiFisources. PhaseNet positioning data can be added to the inbound andoutbound packets sent from these access points to alert the user that aWiFi spot is available. These PhaseNet positioning data are locatedoutside the firewall, enabling users to receive the data even if theyare not logged onto a network, thus enabling both secured and unsecuredaccess points to provide positioning data.

SUMMARY OF THE DISCLOSURE

Reliable sub-meter positioning performance and sub-nanosecond timingcalibration are readily obtained using this PhaseNet based methodology.The method entails weaving positioning data into the fabric of wirelessnetworks. More specifically, the method entails embedding PhaseNet datainto the communications channel of the various wireless networks thatproliferate throughout our environment. WiMax, WiFi, Cellular, MeshNetworks, Ad Hoc Networks, First Responder Radio, and Spaceborne systemssuch as Iridium and the GPS constellation itself are all potentialPhaseNet carriers. Wireline networks are also potential PhaseNetcarriers for use in areas such as wireline IT networks synchronization,geodetic remote sensing, and fiber optic backbone calibration.

Consequently, an end user having only a laptop, PDA, VoIP phone or otherdevice with a WiFi network connection receives positioning data withoutthe need for a costly GPS chip. Cities are deploying metropolitan-wideindustrial grade WiFi networks that enable a WiFi user to maintaincontinuous WiFi access while moving almost anywhere within the city. Thedevelopment of metropolitan-wide WiFi networks in more than 250 citieswill by 2009 create a very large user base for WiFi-based positioningdevices.

Just as WiFi provides positioning signals, WiMax, cellular, broadcasttelevision, digital radio, satellite, and mesh networks are potentialPhaseNet signal carriers. This “last-mile” wireless broadband solutionis like cellular service in that base stations provide the signals tothe user for broad baseline service. Individually, each signal type canprovide robust positioning. If all the signals are synthesized, then theresulting ‘signal soup’ enables almost any communicating device to be areliable, precision locator without requiring an embedded GPS chip.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a pictorial diagram of a preferred baseline embodiment thatincludes 10 cars traveling within a defined space to facilitatedescription of the operation and implementation of the disclosedspace-time calibration technique.

FIG. 2 depicts a monoplex communication link between signal-transmittingcar A and signal-receiving car B.

FIG. 3a shows a general example of network communication links developedamong the 10 cars of FIG. 1.

FIG. 3b is a reproduction of the pictorial diagram of FIG. 1 shown withmonoplex and duplex communication links among the 10 cars.

FIGS. 4a and 4b are, respectively, pictorial and signal waveformdiagrams illustrating use of a zero crossing of a carrier signal toidentify a specific node initiating a ping event.

FIG. 5 represents the function of a count-stamping module operating on anode receiving a ping event.

FIG. 6 is a plot of ongoing pingforms containing data from whichspace-time calibration solutions for the nodes are generated.

FIG. 7 illustrates the growth of a communication network with additionof new nodes that produce additional data and thereby result inoverdetermination of network variables.

FIG. 8 illustrates the entities of the equation representing a receivingnode count-stamping ping event initiated by a sending node.

FIG. 9 illustrates the vector equation g=Hf representing the pingformsproduced over time in accordance with the equation set out in FIG. 8.

FIG. 10 is a diagram showing how pingforms transform into dpingformsthat become entries into the g vector.

FIG. 11 depicts the organization in harmonic blocks of the data entriesof the g vector presented in FIG. 9.

FIG. 12 is a diagram showing the pre-organization of the f vectorpresented in FIG. 9.

FIG. 13 is a diagram showing the organization of the H matrix presentedin FIG. 9.

FIG. 14 is a general block diagram showing major components of aspace-time calibration unit that performs the operations for generatingsolutions of the vector equation presented in FIG. 9.

FIG. 15 is a diagram illustrating aspects of Riccian-Raleigh Quality fornetworks of nodes.

FIG. 16 is a diagram depicting a high quality ping reflection event.

FIG. 17 is a diagram illustrating functional differences between activeand passive nodes.

FIG. 18 is a simplified diagram of an active node structure.

FIG. 19 shows nodes arranged in three exemplary node topologies.

FIG. 20 is a diagram showing an example of network nodes evolving overtime through arbitrary states of network connectivity.

FIG. 21 is pseudo-code representing a highest level organization ofongoing operation of the space-time calibration unit of FIG. 14.

FIG. 22 is pseudo-code representing an outer repetitive process drivingthe main operation of a given node.

FIG. 23 is pseudo-code representing maintenance of an activeunderstanding of local node group topologies such as those shown in FIG.19.

FIG. 24 is pseudo-code for generating f vector solutions.

FIG. 25 is pseudo-code representing managing the operation of ping,pong, and pong data collection and dissemination, as well as monitoringcommunication link quality.

FIG. 26 is a diagram of the use of harmonic blocks in equation andsolution formulation.

FIG. 27 is a diagram showing an example of node-ping interaction.

FIG. 28 shows that solution waveforms based on equally spaced points canbe represented discretely through sequencing of sinc functions.

FIGS. 29 and 30 are, respectively, waveforms and expressionsrepresenting solution waveforms of three harmonic blocks.

FIG. 31 is an example expanding the illustration presented in FIG. 29 toa quasi-simultaneous duplex sequence of pings between nodes A and B.

FIG. 32 is a diagram indicating how the H matrix and associated vectorsare populated by communication among the 10 cars of FIG. 1.

FIG. 33 represents harmonic block organization structures for the f andg vectors of the example depicted in FIG. 31.

FIG. 34 is a version of FIG. 33 reconfigured into five concatenatedsuccessive harmonic blocks for 10 nodes and 90 mono-directional pingchannels.

FIG. 35 is a block diagram of carrier wave count-stamping circuitry of anetwork node.

FIG. 36 is an example of a pingform of pings sent at regular countintervals by the counter circuitry of FIG. 35.

FIG. 37 is an example of a pingform based on reception of pings sent byanother network node.

FIG. 38 is a diagram representing an example of raw pingform datameasured by stationary nodes.

FIG. 39 is a diagram representing stored pingform data organized inharmonic blocks.

FIG. 40 is a set of five graphs depicting pingform compression usingsecond derivatives and run length encoding.

FIG. 41 is a diagram demonstrating determination of a coarse rangeestimate between two network nodes.

FIG. 42 is a diagram demonstrating determination of coarse rangeestimates of more than two network nodes.

FIG. 43 is a diagram illustrating use of echo location to determineboundaries of an external framework.

FIG. 44 presents multiple-epoch waveform equations (MWMEs) in parametricform.

FIG. 45 is a set of diagrams presenting an intuitive representation ofwaveforms corresponding to the waveform equations set out in FIG. 44.

FIG. 46 is a set of diagrams depicting two subclasses of MEWEs,including GPS and local wireless/cellular networks.

FIG. 47 is a diagram depicting the organization of families of Hmatrices.

FIG. 48 presents an example of three H matrix family solutions for thebaseline embodiment of 10 cars described in part with reference to FIGS.3b and 5.

FIG. 49 is a diagram depicting data flow associated with numericallypopulating H matrix rows.

FIG. 50 is an abstract rendering of the H matrix organization depictedin FIG. 13.

FIGS. 51 and 52 are simplified diagrams showing examples of two variantson a nominal inverted H matrix.

FIG. 53 is a diagram demonstrating a node using classic range circles todetermine its location.

FIGS. 54 and 55 are diagrams demonstrating a nominal solution approachand presenting nominal solution waveforms, respectively, for thebaseline embodiment of 10 cars.

FIG. 56 is a diagram depicting the generation of solutions foroverlapping nodes.

FIG. 57 is a diagrammatic metaphorical rendering of an Earth-like planetsharing a common asymptotic collision frame with a neighboringrelatively low mass black hole.

FIG. 58 is a diagram depicting coarse vectors of nodes under static anddynamic conditions.

FIG. 59 is a diagram of 2 nodes shown isolated in a 10-node network todemonstrate metric space solution error resulting from node motion.

FIG. 60 is a set of two diagrams illustrating two alternative examplesof topologically defined metric spaces for the baseline embodiment of 10cars.

FIG. 61 presents car travel paths illustrating a well-defined range ofasymptotic location solutions neighboring an available nominal locationsolution.

FIG. 62 is a plot of location solution waveform families delay vs.precision/accuracy.

FIG. 63 is a graph showing clock counts corresponding to twotime-displaced ping events transmitted from a first node and received bya second node under conditions in which the nodes are the same distanceapart from each other during two ping events.

FIG. 64 is a graph showing clock counts corresponding to twotime-displaced ping events transmitted from a first node and received bya second node under conditions in which the nodes are differentdistances apart from each other during the two ping events.

FIG. 65 is a graph showing clock counts corresponding to twotime-displaced ping events transmitted from a first node and received bya second node under conditions in which the nodes are the same distanceapart from each other during the two ping events but their clock ratesare dissimilar.

FIG. 66 is a diagram for use in illustrating the calculation of a changein distance between the first and second nodes.

FIG. 67 is a diagram showing two timelines of ping events transmitted bythe first node and received by the second node for calculating changesin rates of the clocks of the first and second nodes.

FIG. 68 is a diagram showing two exemplary harmonic block intervals forsolving a matrix equation generating solutions for changes in clock rateand node location.

FIG. 69 is a diagram showing an example of a solution path formed byconcatenation of three solution path segments of a node and thecorresponding actual path traveled by the node.

FIG. 70 is a diagram showing an example in x, y coordinate space therepositioning of new asymptotic solutions to finish locations ofprevious solutions for two nodes.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

This disclosure describes a simple example of an overall PhaseNetembodiment that explicitly uses sound waves as opposed toelectromagnetic waves to communicate between PhaseNet nodes. It alsoexplicitly affords a tutorial treatment of how PhaseNet works, mostnotably in the use of relatively common “low and medium tech” methods,without sacrificing critical underlying engineering principles requiredfor enabling electromagnetic-based PhaseNet operations as well. Theextrapolation of audio principles to electromagnetic (EM) methods is agenerally straightforward exercise, with the details of count-stampingcircuitry and hardware being the primary physical difference betweenaudio-based and electromagnetic-based implementations. The details ofhow the physical system-agnostic principles work on leading-edgesatellite systems or terrestrial wireless and cell networks can allreference back to this simple enabling example using audio.

FIG. 1 depicts an example of a preferred embodiment that entails tenrandomly distributed robotic cars moving within a defined space, e.g., agymnasium. These cars are equipped with conventional drive and steeringmechanisms, and they communicate with one another using free-space, 2-Dplane, or full 3-D omnidirectional audio signaling equipment. The carsalso are equipped with a set of core PhaseNet-specific elements andsoftware procedural engines. These elements are packaged together as aprototype Space-Time Calibration Unit (SCU), which collects real-timeinformation from the car on which the module is mounted, and fromneighboring cars, and fashions a space-time solution from theinformation gathered. The number of cars in the system is an arbitrarychoice.

Various experiments described below test different combinations of thenumber of cars that are actually communicating, and how PhaseNetprinciples function as nodes turn on and off, or as effectivecommunication links between cars become intermittent or shut down.

Each car includes a core counting element that includes a low costdigital clock with an associated digital counter. The counter is drivenby the clock; it should have at least a 64 bit counting range. For abaseline embodiment, it can run at roughly 1 million counts per second.The core counting element can be built using cascades of counters, with8 or 16 bit counters running at the highest speed, and driving lowerrate 64 bit counters, for example. These specifications are quiteflexible. Given that PhaseNet solutions ultimately solve for count-ratevariabilities between PhaseNet nodes, their quality can be commensuratewith extremely low-cost parts and very basic performance specifications.

FIG. 2 shows the basic audio equipment used for duplex and monoplexcommunications between the cars including a single omnidirectionalspeaker (that spreads sound wave energy in the two-dimensional plane ofthe gymnasium floor) and one or more omnidirectional microphones.Various experiments showing the continuity of PhaseNet space-timesolutions will turn these audio instruments on and off.

FIG. 2 highlights that the combination of a speaker on a first car Awith a microphone on a second car B—representing a monoplexcommunication—while A also has a microphone listening to car B, thusproviding the basis for duplex communications. Experiments describedbelow exercise various combinations of both monoplex and duplexcommunications, illustrating how PhaseNet solutions can routinely dealwith any combination of node communication configurations.

There are many possible choices for audio communication protocolsbetween cars. The minimum digital communication requirement for amonoplex or duplex channel in a preferred embodiment may be the abilityto transmit 10,000 bits per second (bps). Normal telephone-line audiomodem protocols are the specific example used in this embodiment, wherethe state-of-the-art functional bit rate for such equipment issignificantly higher than 10 Kbit per second (10 Kbps). 10 Kbps will besufficient for this embodiment, however.

PhaseNet operates using generic D3P or duplex ping-pong-pung signalingprotocols. In this embodiment, pings will dominate the raw generation ofspace-time data, pongs will be used for the purpose of determining wherethe outside walls of the gymnasium are located, and pungs will be partof the digital communications between cars.

In short, a ping is any network-agreed method or multiple agreed methodsof generating an event at a car, count-stamping that event using thecar's local counter, and communicating the event to another car thatcount-stamps the arrival of the event. This is essentially the ideabehind GPS-satellite-to-GPS-receiver signaling. A pong is similar to aping in that a car count-stamps and communicates (often broadcasts) anevent, but the event is reflected back to the event-generating car byits external environment or to another car, to be subsequentlycount-stamped by the event-generating car upon arrival of one or moreechoes. A pung is a digital packet of information sent between two carsor sent by one car broadcasting to all others that might be listening.It is called out as a discrete entity to highlight its most common formof a modern digital packet or some fractional payload within a digitalpacket.

Skilled persons will instantly recognize that pings, pongs, and pungs,entities as defined above, are in very wide use in many systems today,under different names appropriate to specific applications and systems.The central reason for creating these entities within PhaseNet is toaggregate and optimally exploit this rich source of prior methods, whileabstracting and generalizing their basic information content across allsystems and spatial-scales. PhaseNet can thus operate on these genericentities rather than taking into account specific details of thephysical instruments being employed, or details of the specific mediumof communication or its inherent topology. In short, nodes move about ina soup of pings, pongs, and pungs, whether that soup is composed ofacoustic waves from a semiconductor integrated circuit or laser pulsesfrom a space probe at the edge of the solar system. PhaseNet is designedto work no matter what the soup contains.

Each audio signaling protocol supplies its own precise definition of a“ping event.” The main concerns in making this definition are the noiseproperties relative to a node's ability to count-stamp an outgoingbroadcast ping as well as count-stamping an incoming received ping,using its local digital counter as the component that effectivelyenumerates the ping event. “Time-stamp” is an intuitive cousin to “countstamp,” but PhaseNet has a strong preference for the phrase“count-stamp” because the notion of “time” is asymptotically defined.Across the vast range of PhaseNet application and configurations, thereis an equally vast range of “ping” definitions, with general ideasbehind “carrier phase” methods within GPS playing an important leadrole. The description below touches upon carrier phase methods even inthis rather straightforward baseline audio embodiment.

It is not necessary to literally impress a dedicated ping signal or“pulse-like spike” directly onto the chosen audio signaling protocol.The normal operation of an audio signaling method has certain regularproperties that can be defined as the ping event, such as specific zerocrossings of a carrier signal or the tagging of a specific symbol orpreamble sent out over the audio communication channel. Carrier phasemethods within GPS receivers are prior art in this regard (as alluded toabove), in which the normal oscillation of the GPS carrier signal isused as a tagged event, as well as code symbols sent out as an explicittime-stamped event.

Though it is unnecessary to impress ping events onto a communicationsprotocol, neither is it precluded. In certain situations requiringprecision, one may wish to modify a given communication protocol to moreexplicitly and physically create a ping event, generally with the intentof improving the precision with which an outgoing ping iscount-correlated with its local digital counter. An example of this isthe creation of a simplified waveform such as a de-modulated deltafunction or rise-time waveform where precision methods of measurementcan be applied.

In the current embodiment, conventional modem protocols are assumed tobe the communications mechanism between cars, using the speakers andmicrophones described above as the physical channel. Provisions are madefor slight Doppler shifts in the primary carrier frequency of suchprotocols, because the cars may travel up to a few meters per second inrandom directions (which in the current embodiment may be ofinsignificant consequence, whereas in other applications such as motorvehicles on an open road and aircraft communications, Doppler issuesbecome more pronounced). Most modem equipment has such provisions tocope with slow drift or offsets in carrier frequencies, typically in theform of phase-locked-loops having reasonable capture ranges on thecarrier frequency.

For the purposes of simply demonstrating how PhaseNet functions, it canbe assumed that digital data carried by audio communications include twoprimary payloads: (1) data used to actually drive the car if it is notautonomous; and (2) pung data packets which, in this embodiment, areessentially the sharing of local count-stamps of sent and received pings(with significant “compression” as will be seen, because theredundancies in raw ping data need not be communicated). OverallPhaseNet monitoring data and node-health data are also part of thedigital communication channel, and explicit companions to the ping datawithin the pung packet.

Also included in the inventory of core elements on a given car/node, arethe physical/electronic components that actually correlate the localdigital counter with both broadcast ping events and the incoming pingevents on individual audio microphones. The output of these componentsis a digital stream of count values, representing values of ping eventsbroadcast via the car's speaker, and count values of ping events beingreceived on microphones. Up-to-date implementations of these componentscan use digital waveform generators and a digital to analog (D/A)converter for generating and sending out pings and pungs. Likewise, asimple analog to digital (A/D) converter on received audio is quitestraightforward. Skilled persons will generally recognize that all thatis being described here are simple forms of what is now called“software-defined radio.”

There exists master software that runs the ongoing processes furtherdescribed below, monitors the health of the node and its immediatenetwork, stores data, and in certain situations, oversees or evenconducts calculations of space-time solutions. For the preferredembodiment, this software is generic personal computer software that caneasily operate (keep up with) processing the flow of informationinvolved. As other embodiments are considered, this software can becomemuch more complex and it can be segmented into various physical modulesand locations.

The act of actually calculating space-time solutions in real time mayovertax the numerical capabilities of certain CPUs, thus necessitating asecond parallel CPU on a car dedicated to numerical calculations asdescribed below. Indeed, a sophisticated robotic car may have many CPUs.

Runtime Operation

By putting the pieces together, one can see the operation of individualcars, quickly culminating in all ten cars moving around and undergoingthe ambiguities of links and nodes going on and off-line. The end goalis to illustrate PhaseNet's functioning both as it allows cars todetermine where they are relative to one another, as well as separatelyillustrating how they can situate themselves relative to their knownuniverse of the gymnasium.

First, counters are switched on and begin counting at a million countsper second. All cars can “start their counting engines” whenever theyplease, but at some point all of them are counting, generally atslightly different count rates and with randomly different count values.Later experiments will deliberately impose conditions on poor anderratic count rates in order to illustrate the extreme flexibility builtinto PhaseNet, but for the purposes of the current embodiment, one canassume that the counters and clocks being used are of reasonably highquality and generally within a fraction of one percent of each other, asdefined by an objective calibrated observer in the gymnasium.(State-of-the-art EM systems may not have the luxury of an externalmaster calibration, where this is a core subject in the discussion onhow PhaseNet can potentially be a tool in the continual evolution ofglobal time and space standards.)

Next, assignments among cars are made. There are many ways to do this.Specifically, combinations of both fixed schedule linking and unlinkingcan be tested, as well as adaptive behavior responsive to real-timeconditions, such as one car occluding another. FIG. 3a is a plot showingwhich cars are communicating and whether the channels are bi-directionalor one-way. Note that there are explicit gaps and that the operation ofPhaseNet is meant to function within arbitrary and ever-changingpair-wise communication networks, though FIGS. 3a and 3b depict ageneral case of arbitrary communications. Later, the explanation startswith all cars having duplex communication capabilities with all othercars initially to illustrate basic PhaseNet operations, followed byselectively turning on and off links in the resultant network andgetting back to forms that are closer to FIGS. 3a and 3 b.

Once initial communication assignments are in place, each carsynchronizes, at least in a digital communication (modem) sense, to itsassigned partners. Each car begins to broadcast a simple repetitivemodem-like signal to which the other cars can tune, following awell-established modem ritual. The easiest way to isolate individualcar-to-car communications is to assign specific audio carrierfrequencies to pairs of cars. But there are also ways to have thenetwork share a common carrier frequency and use spread spectrum methodsor other kinds of shared medium methods to nevertheless instantiatecar-car monoplex and duplex channels. Indeed, when moving from thisembodiment to various state-of-the-art wireless and cellularembodiments, the options become as numerous as the panoply of moderncommunication systems allows.

In the mono-directional assignment case where a car is simply listeningto the broadcast signal of one other car, this means tuning into thebroadcasting signal of its assigned car and ensuring that the repetitivesignal is being properly received and decoded. In the bi-directionalassignment case, the listening car B starts to supplement itsinitialization message with a few bytes of information broadcastspecifically to let car A know that it is receiving car A's signal, andexpects to hear from car A to find out whether car A is also hearing anddecoding car B's signal. In general, these are all rudimentary andstandard operations for establishing an arbitrary network ofcommunicating nodes and are very well known engineering issues for bothnetwork and communications engineers.

When all nodes are cooperating and sending out a network-wide messagethat given car has established its assigned channels and that all itsassigned channel nodes are reporting the same information, thepre-assignments are known to be complete. Once all pre-assignments ofcommunications partners are established, the count-stamping componentryon all cars can start to count-stamp outgoing pings and incoming pings,if it has not started doing this already. That componentry can begin atany time; it simply needs actual communications running before the countvalues have meaning.

Looking at a car D, the local clock that is driving the digital counterhappens to be the clock that is driving the 50 Kilohertz centralfrequency on its modem communications channels. Since in this example ithas been determined that each car will produce 1,000 pings per second(distinct from, but loosely related to the previously chosen 10 Kbps),the “ping event” for car D has been determined to be every fiftieth zerocrossing of its central frequency. It thus automatically produces thevirtual 1,000 pings per second by simply oscillating as usual. Becausethe digital counter and the modem are running off the same local clock,it is known that there are exactly 1,000 count values (1 million dividedby 1000) elapsing between each transmitted ping (every fiftieth zerocrossing). Thus, the count-stamping of the outgoing pings is alsoautomatic. Practitioners of GPS equipment design will recognize distinctsimilarities of these ideas to GPS code phase and carrier phase priorart, and they may anticipate engineering challenges such as keepingtrack of “every fiftieth cycle,” cycle slips, carrier phase ambiguity,and the like. Except for one example, data-driven modulation of thecarrier frequency itself gives rise to pseudo-noise on the uniformity ofthe fiftieth zero crossing, requiring a sub-process to determine andeffectively remove the pseudo-noise prior to executing the core PhaseNetalgorithm. Later descriptions explore these challenges in thissimplified gymnasium setting using audio waveforms.

(Note that the speed of sound is just over 30 centimeters permillisecond, thus ensuring that cars separated by several meters willhave non-trivial delays or count intervals between transmitting andreceiving a ping. This simple point is centrally important in thesubsequent extrapolation of this embodiment to satellites, in thatsatellites several hundred or several thousand kilometers apart willencounter similar multi-count delays in pings-sent to pings-receivedeven when electromagnetic waves traveling at 30 cm/ng are used.Automobiles on a “smart highway” that can alert a car to the aberrantsub-millimeter-level fluctuations of a drunk driver approaching fromseveral hundred meters away would need to account for these non-trivialdelays in light signals over sub-kilometer distances.)

In one detailed scenario, a car D happens to be listening to five othercars with its microphone. In establishing communication with all theother cars, each car informs car D of exactly which zero crossing of itsunique near-50-KHz central frequency corresponds to its ping event. Avariety of methods could accomplish this, but perhaps the simplest oneis to explicitly modulate a code symbol onto the carrier signal suchthat the very end of the code symbol corresponds to the desired zerocrossing, later to be enumerated through a separate digitalcommunication. FIGS. 4a and 4b concisely summarize the situation. Itshould be noted that GPS carrier phase/code phase methods are the moresophisticated precedent to follow in establishing these code/carrierrelationships, both in this simple car example and in a more complexexample that entails high speed satellite links.

FIG. 5 summarizes the function of the count-stamping module on car Dthat uses car D's local digital counter to enumerate receipt of pingevents arriving at a given microphone. A variety of commercial circuitrycan be used to accomplish this count-stamping function, and differentmethods may be applied to illustrate the many possible ways to create“pingforms” given a specific communication hardware solution andcommunication protocol. The output of the count-stamping module(software or hardware) is a sequence of count values. In the simpleenabling example of this section, integral count values with an inherentresolution of 1 ms provide for sufficient resolution on the pingforms(nominally 0.3 mm per tick of the counter, as projected into free-spaceaudio waves). In precision picosecond-level applications using counterswith inherent resolutions surpassing the microsecond level, fractionalcount value capabilities are fundamentally required and need to begenerated commensurate with the Gaussian noise levels on thecount-stamping operation itself. Skilled persons will recognize that theact of counting is ultimately separate from the capability to timestampan electronic signal, and thus fractional count-stamping will be thenorm in EM systems. This embodiment deliberately uses a count-rate thatavoids this secondary engineering issue so as not to obscure integralnumber mathematics and programming by a floating point haze.

FIG. 5 also briefly introduces the important topic of “delay” inphysical circuits and its relationship to signals actually transmittedby antennae. Two separate kinds of high level delay are depicted: thedelay that occurs between a signal inside a pre-amplifier and itsamplified version exiting the antenna, labeled ‘delay 1’ in the figure;and the delay between the same signal in the pre-amplifier and theproduction of a digital value within the counter during a query pulse.These delay concepts are introduced for completeness; later sectionsdescribe the way in which these delays are explicitly dealt with. Aswill be seen, the emphasis on differential space-time solutions overabsolute space-time solutions all but eliminates most consequences offixed delays in circuitry, where variations in delay represent anuisance and an eventual source of error in space-time estimates.Asymptotic methods will be presented that outline how fixed delays canbe made immaterial to absolute space-time solutions, for example, whenan object needs to be placed within the GPS frame.

Note also that existing communication hardware typically does notinclude one of these “ping pulse generators,” as depicted in FIG. 5, butthey can be adapted in the future to accommodate PhaseNet.Time-of-arrival (TOA) enabled communications equipment indicates thatsteps have been taken in this direction, whereby signals are tagged forcertain events and those events submitted as query-pulses to counters.In the case of the preferred embodiment, the audio waveforms are tied tothe sampling rate of the D/A converter chip, and thus raw count valuesof the D/A chip can function as the output sequence of digital countvalues.

FIG. 6, a companion plot to FIG. 3a , shows pingforms plotted alongsidethe assigned communication pairings. As described earlier, the datacontained in FIG. 6 are the foundation for the space-time calibrationsolutions describing the dynamic evolution of the cars in the gymnasium.In many ways FIG. 6 is a poster-child for the ‘observables only’approach to space-time construction, where space and time are derivedconcepts based on only the pingforms of FIG. 6.

Though low cost digital clocks can provide sufficient accuracy for thissimple example, they may be deliberately perturbed. The flexibility ofPhaseNet can thus be demonstrated, because a primary function ofPhaseNet is to sleuth and solve for the inter-variability of count ratesbetween PhaseNet nodes, amidst a cacophony of influencing factors suchas dynamic spatial relationships, environmental factors, instrumentdrifts and delays, and eventually gravitational and relativisticperturbations. Without deliberate perturbation on count rates alluded toabove, this enabling example would still come up with solutions forcount inter-variability, but it would represent almost a trivial levelof solution for clock irregularities relative to the spatial dynamics ofthe cars.

Thus, for this enabling example, deliberate perturbations to the digitalclocks highlight the ability of PhaseNet to measure count drift, anexercise that portrays the same issues when dealing with EMcommunications and the system engineering goal of reaching single digitpicosecond network synchrony. Measuring higher bandwidth phase noise onindividual PhaseNet nodes has pragmatic implications to signalacquisition and tracking, as well as communications bandwidth andquality of service. The phase noise associated with a given node canbecome a network-defined variable and can begin to have real-timemeasurements approaching the chosen ping-rate. The measurement of driftconsumes the higher bandwidth phase noise due to a web of communicationlinks, as illustrated in FIG. 7.

Pingforms into Space-Time Solutions

The primary data generation aspects of this embodiment are representedin FIG. 6, supported by the communication-pair assignments representedin FIGS. 3a and 3b . FIG. 6 depicts the collected set of pingforms. Itremains to convert these raw data into matrix forms, which can then castnetwork parameters such as distances and directions between nodes andinter-variability of count rates as solutions to the collected data.

Counting and Communicating Construct Space-Time Solutions

A central conclusion of “set physics” is that a set of elements (nodes)having the ability to count, the ability to communicate their counts,and the ability to associate count values received by other nodes withtheir own internal count, may define, based solely on structures similarto the pingforms of FIG. 6, the distance, direction, and orientation, or“space-time” describing the motion of elements in the set.

The notion of “absolute” space-time is unsupportable because (A)space-time is not defined for a null set or a single element set, and(B) space-time is defined for a populated set only after communicationshave taken place between set members, and the definition can refer toonly a space-time description of an asymptotic past state of the set.The fact that it is fundamentally impossible to define the current stateof the set because only active in-process communications can do that isreferred to as the “asymptotic collision frame.” As records of morecommunications are accumulated, past states of the entire set can bedetermined, and collisions between set members can be a set-wide definedphenomena rather than a mathematical construct of relative motion. Thisbecomes an important practical matter as PhaseNet is scaled tosolar-system spatial scales.

In the context of the 10-car example, the metric that PhaseNet createsas the companion to the pingforms of FIG. 6 include changes in positionand direction between the cars (explicitly including car motions ofright/left and front/back), as well as the variability of count ratesbetween cars. In fact, a fundamental metric is the variability itself.

There are several specific approaches to mathematically expressing thespace containing a set of elements. A fundamental equation that relatesthe metrics described above to the pingforms of FIG. 6 is:

darrival(a,b)=dcount(a)−dcount(b)+ddist(a,b).  (1)

This ideal equation represents car A count-stamping the arrival of aping sent from car B. The d's in front of the four primary entitiesindicate differential quantities or the changes from one samplinginstance to the next. Sampling rates in PhaseNet generally conform to agiven node's ping rate, and thus the primary entities refer to changesfrom one ping to the next. The primary measured quantity on the leftside of equation (1) is ‘darrival(a,b)’, with an italicized arepresenting the fractional count value at the arrival of the ping atcar A, using car A's counter, while the b is not italicized because itrepresents the integral value of a count by car B (the ping was sent outby B precisely on integral values even in EM systems, whereas with theaudio baseline embodiment even the a will not actually be a fractionalcount value). Later sections will explore error sources and non-idealimplementations of equation (1), but here the focus is on the idealsituation. The primary entity ‘darrival(a,b)’ is thus the discrete ordigital slope of the pingform of car A, at the arrival point of the pingsent from car B. The use of the slope obviates the issue of differentstarting count values for the cars. More importantly, it avoids laterquadratic equations and roots of combined quantities arising frommultiple spatial dimensions if formulated in “background frame” absolutequantities. The primary entities ‘dcount(a)’ and ‘dcount(b)’ representthe slopes or rates of change variables in the count inter-variabilitycurves (part of the metric chosen) while ‘ddist(a,b)’ is the slope ofthe distance, or the rate of approach or recession between car A and carB. It is not unreasonable to relate ddist to a Doppler signal.

Critics might be quick to ask “change with respect to what?” when theyconsider that isolated variables (e.g., the variability of car A'scounting) are being measured; what is the external standard being usedto set the context for this variability? This is a very reasonablequestion especially relative to the introduction of equation (1). Thechange is the primary entity in the particular metric space chosen.There is no need to isolate one particular car/node's counting anddemand that a master car drive up along side it and judge its countingcapabilities (uniformity of rate, rate itself, etc.). Change itself canbe a candidate primary entity—an explicit choice for a metric; a dx isjust as good as an x to start building equations and solutions to thoseequations; each is just a symbol for an entity, and there is no need toburden the entities with axiomatic relationships.

Building the baseline embodiment of the moving cars, exchanginginformation, with eventual resultant measurements of change being thefundamental entities measured within the system is mathematicallystraightforward. The sequential differences in the intervals betweenarriving pings can be accounted for through three core mechanisms: ratenon-uniformities in the receiving node at the instance of arrival of theping, rate non-uniformities in the sending node at the instance ofsending the ping, and “spatial movements” of, independently, the sendingnode at the instance of sending the ping and the receiving node at theinstance of receiving the ping. With this equation, there is an initialconnection between acquired data and a ‘pseudo-metric’ in that distancestill needs to be more practically rendered.

Toward this end (practically transforming ‘distance’ into somethinguseable), for the case of the cars, a two dimensional space is definedusing ‘change entities.’ The introduction of two-dimensional space isrepresented by equation (2) and the accompanying graphic FIG. 8:

darrival_(A)(a,b)=dcount_(A)(a)−dcount_(B)(b)+(dx _(A)(a)−dx _(B)(b))k_(x)+(dy _(A)(a)−dy _(B)(b))k _(y),  (2)

in which the ddist variable of equation (1) has been replaced withfamiliar Cartesian x/y variables, k values, and subscripts indicating towhich car the variable belongs. The primary Cartesian entities are thechange in position within a two dimensional space tied to the instancesof arrival of the ping by car B for car A and the sending of the ping bycar A for car B. The minus sign in their combination is needed byinspection of FIG. 8. The equation appearing in FIG. 8 has a ‘(1/c)’constant in front of the Cartesian expressions; equation (2) insteadplaces the relationship between space and time coordinates into the kvalues to keep equation (2) as simple as possible.

The subscripted k values are essential to two-dimensional and higherdimensional formulations of PhaseNet. This disclosure refers to theentity k_(xy) _(_) _(ab)={k_(x), k_(y)} as the “coarse direction vector”existing between car/node A and car/node B. The word ‘coarse’ is usedbecause strict direction is only asymptotically defined, and yetdirection can be utilized nevertheless. In its strictest form, thecoarse direction vector is simply the starting estimate on a convergencesequence, but for all practical purposes, a small percent error in thedirection vectors is trivial compared to error analysis.

FIG. 8 graphically depicts the coarse direction vector. With theintroduction of coarse direction sectors, equation (2) can beinterpreted to say that sequential differences in the intervals betweenarriving pings can be accounted for through six distinct entities: thesame two dcount entities from equation (1), and four additional “spatialchange” entities of the two nodes, tied to sending and receivinginstances of the two. Each 2-D datum point has associated with it sixmetric unknowns. In three dimensions, one datum point will have tenunknowns: two counting-rate unknowns and 2×3 spatial change unknowns. Atthis point it can be seen that one of the main roles for the coarsedirection vectors is to establish a Cartesian coordinate system suchthat motion can be resolved into two orthogonal components that makesense to both nodes (and eventually the entire set of nodes).

Mathematically, there is no reason to require interpreting spatialchange with respect to a physical framework other than what is impliedby equation (2). All ten of the cars can roam around, pinging eachother, collecting data in the form of the left-hand term of equation(2), and then start to ask how the unknown entities on the right handside of equation (2) can begin to resolve themselves as more data arecollected.

Duplex Pings and Initial Metric Solutions

With reference to FIG. 3b , the baseline embodiment of the ten cars,when all ten cars have started to count, it is assumed all cars are induplex communication with all other cars (although normal operationentails arbitrary configurations of duplex and monoplex links, as in thegeneral case depicted in FIGS. 3a and 3b ).

Each car listens to and records the other 9 cars' pings, yielding 9×10or 90 pingforms that are then recorded. At the same time, there arethree basic metric unknowns for each car, giving a total of 3×10 or 30metric unknowns. Without consideration of data production rates andmetric solution rates, the baseline embodiment uses a 3× over-samplingratio as the set evolves. Each new connection adds two new pingforms toan otherwise constant network. Each addition of a new node adds threenew unknown metric waveforms, and up to 2×(N−2) new data pingforms,where N is the number of previous nodes in the set. This gives a firsttaste of one of the primary mechanisms by which PhaseNet can enhanceoverall system precisions and accuracies through Metcalf-like networkingprinciples.

FIG. 9 depicts how a set of 90 evolving pingforms can be recast inmatrix a form, the right-hand side of equation (2) supplying as thecoefficients in the matrix. This is a solvable matrix equation givensufficient data collection, after many seconds have elapsed. However,the matrix equation becomes impractical as the matrix size increases.

The simple form of the matrix equation relating acquired data to achosen metric space is given by

g=Hf,  (3)

in which the vector g represents the total number of pingformdifferences collected, or dpingforms. FIG. 10 explicitly shows how thepingforms can transform into initially organized sets of dpingforms.There exist several choices of how to order the raw darrival values thatmake up the dpingforms.

One choice is to order the darrival values (vector g) in groups of 90,corresponding to roughly synchronous pings of the 45 duplex channelsexisting amongst the 10 cars/nodes. There is nothing inherently wrongwith this ordering choice. PhaseNet organizes the g vector in shortsnippets of dpingforms, of a size equal to the length of the “harmonicblock.” In the baseline embodiment, this length is defined as 10milliseconds or nominally 10 ping epochs for any given car/node. MostPhaseNet implementations will probably choose between 5 and 100fundamental ping epochs per harmonic block, being a trade-off between,on the one hand, flexibility in dealing with different sampling rates onthe chosen metrics, and on the other hand, inverting very largematrices. As computing resources continue to improve, the choice willslowly move beyond “100”, as the pressure to worry about the size ofmatrices and the speed of inversions lessens.

FIG. 11 explicitly depicts the chosen organization of the g vector ofequation (3). Note that dpingform snippets are organized in duplex pairsfirst, followed by arbitrary sequences of duplex pairings. EM-basedembodiments of PhaseNet will organize the duplex pairings according to awide variety of criteria in which, mathematically speaking, anyorganization scheme is the same as another so long as the rawinformation contained in the collective dpingforms is present. Certainmatrix inversion schemes, especially those that exploit sparse matrices,also factor into decisions on g-vector organization.

Vector “f” on the right hand side of equation (3) is the metric solutionvector made up of the entire set of the right-hand-side variables ofequation (2) (not including the coarse direction vectors, which will bediscussed below) collected from the time that all cars started to recordpings to the time when a solution snapshot is attempted with use ofequation (3). FIG. 12 depicts one kind of pre-organization of this fvector. The “pre-organization” simply refers to the fact that the figuredoes not depict how this can become a 1×50 vector. One feature of thisorganization of the f metric vector is that the snippet lengths of thedx and dy solution waveforms are only two points for each harmonic blocklength, and the snippet lengths are only one point for each harmonicblock for the dcount solution waveforms. The general PhaseNet principlethus illustrated is that metric solution waveform rates need not bematched to the ping production rate, and can in fact be any integralvalue within the harmonic block length. It also turns out that pingrates can be any integral number of pings per block, though in thebaseline embodiment all cars have been chosen to run at 10 pings per 10millisecond harmonic block.

The f vector is also ordered in an essentially arbitrary enumeration,with car A naturally coming first and so on. Any given car then has2×2+1 or 5 metric unknowns per harmonic block, while the g vector forthat given car is producing 9×10 or 90 data points per harmonic block.There exists here another kind of over-sampling that is effectively thesame as time-averaging or filtering signals in classic signalmeasurement systems. But with PhaseNet, one has finer control over howthe raw data filtering is apportioned into solution vector error, withthis example showing that there is a distinct difference betweenmeasurements on spatial changes relative to count-rate changes. This isintended to be a simple example, and mixed-rate solutions can becomemuch more sophisticated in general EM-node PhaseNet configurations.

The H matrix term of equation (3), depicted in FIG. 13, can becharacterized by relationship between the g vector and the f vector inequation (2), given the choices of organization of both vectors, withthe following additional components: (1) the coarse direction vectork's; (2) stringing harmonic blocks together into larger matrixequations, covering longer periods of data collection; (3) keeping trackof harmonic block spillover because pings are sent in one block andreceived in another; (4) anticipating that at least bi-linearinterpolation is used in detailed implementations of equation (2),whereby a fractional count value ping receipt implicates at least two(if not three or four in cubic interpolation) solution parameters; (5)one preferred form of dealing with the “past” frayed edge, wherebyKalman-type filtering information is explicitly applied as an input intothe main H matrix equation as a strong bias on the next solution epoch;and (6) a symmetric but very different preferred form of handling the“future” frayed edge, whereby a small zero-bias is added to new solutionparameters that are being introduced but barely sampled, in which thezero bias will eventually be orders-of-magnitude overwhelmed by actualdata-based sampling in the ensuing harmonic block epochs. This latterzero-bias is akin to straightforward low-pass filtering, designed to bemuch less than 1 dB of signal measurement attenuation, yet stillproviding useful matrix singularity regularization properties.

FIG. 13 thus depicts two complete harmonic blocks' worth of the“launched ping” perspective of an H matrix, giving 1950 equations thatinclude the double frayed edge pseudo-equations, and 150 parametricunknowns. For the sake of keeping FIG. 13 as simple as possible, itshould be noted that there is an inherent assumption built in by havingonly one block of ping spillover: all cars are within a three meterradius when they are functioning. This is ultimately not going to be thecase when the cars are let loose, and the spillover blocks will thusbecome N, with N×3 meters being the outside radius of the longestdimension in the gymnasium. The importance of the zero bias term on thefuture frayed edge can be better appreciated when considering this moretypical situation.

According to basic statistics, two harmonic blocks occur in 20milliseconds, each block containing 90×10 or 900 dping data points aswell as 10×5 or 50 solution metric points. Putting both blocks together,the 20 milliseconds of data collection creates a 1,950×150 sparsematrix, that can be solved to measure the space-time characteristics ofthe cars during that 20 milliseconds. This is a reasonably sized matrixto invert, provided inverting it at a rate of “many-times-per-second” isnot required. The section on H-families and inversion explore thisspecific topic further.

Metric Solution Challenges

One challenge is that the matrix H in equation (3) is always singularbecause it is formulated initially using only rows derived from equation(2). Consequently, the matrix H is non-invertible and theoreticallythere is no single solution, f, to equation (3) but rather amulti-dimensional sub-space family of solutions. One illustration of whyequation (3) must always be singular is as follows: all counters on allten cars speed up at a rate proportional to their distance from theaverage location of the ten cars, while at the same time all the carsbegin to uniformly move away from the center of the ten cars at a ratealso proportional to the distance from the center. The dpingforms arethen identical to the case where all the cars are standing still. Thereare other such interesting singular modes. It will be seen that thischallenge is quite manageable using Moore-Penrose type matrix solutionsas well as general Singular Value Decomposition (SVD) analyses of the Hmatrix, along with appropriate “external knowns” applied to how thesingular properties are regularized. Kalman-type filtering as well asthe gentle (i.e., small) zero bias are designed to provide band-aids tothis singularity issue, but the main thrust in handling it comes fromexplicit singularity-mode regularization.

A second challenge is referred to as the “double frayed edges” problemthat is evident in equation (2), effectively a row in the matrix. At thestart of the one second within the first harmonic block of pings, allcars are recording pings from prior harmonic block epochs, and in thelast harmonic block, pings have been sent out from the cars during thatepoch, helpful in solving the metric parameters of that epoch, yet thepings have not yet been received and recorded by other cars and hencehave not been included in the two primary harmonic blocks. Severalapproaches to managing the double frayed edge challenge will bepresented, with the preferred approach already alluded to in FIG. 13,rendering the challenge to merely a source of quantifiable residualerror in solutions. Those skilled in the art of error estimation canrefer to Fisher information matrix analysis and eventual Cramer-Raobound analyses, an exercise that is achievable in a PhaseNetformulation, but becomes more complicated once details of “past epoch”and “future epoch” treatments are properly considered.

A third challenge is the simple but nevertheless subtle interpolationissue when mixed rates between data production and solution pointproduction are used. The metric solution waveforms within the f vectorare discretely sampled on the one hand, but are effectively representinga band-limited pseudo-continuous wave on the other hand. As in classicdigital signal processing, sequences of digital samples are viewed asNyquist-band-limited sinc functions, linearly combined. The practicalimport of this is that certain interpolation methods must be used inconstructing the H matrix, which tends to add non-zero elements to the Hmatrix, making it less sparse.

A fourth challenge that is not necessarily contained in equation (3) butis almost always present in any practical application, is how to situatethe resultant f vector in an agreed framework. This challenge is thesubject of an entire section below. In summary, a wide range of optionsexist that are fundamentally incorporated with the PhaseNet protocols.The H-matrix organization of FIG. 13 does not require such externalframework considerations to compute the snippet pseudo 20 millisecondsworth of differential space-time solution.

A fifth challenge is that space-time solutions are formed not for merelytwenty milliseconds but for an indefinite time period. Given the doublefrayed edge challenge, a preferred solution handles networkinitialization non-uniformities on the one hand, and as time progresses,collects enough information to make snapshot solutions for past epochsof the overall network. GPS engineers will note a similarity with whatmotivated the introduction of Kalman filtering, first explicated in1960. Kalman filtering plays a strong role in optimizing PhaseNetsolutions, extended to mixed-rate, mixed-epoch interacting waveforms asopposed to the more familiar advancing and discrete system-stateformulation.

A final highlighted challenge is that all the cars move, and linksbetween cars are always changing. This means that the H matrix mustadapt in time to the coarse geometric configuration of the network,including the existence and non-existence of the ping-links between thenodes themselves. This particular challenge gets to the heart of theadaptability of PhaseNet to ever-changing conditions, including, but notlimited to, link existence, link quality, network configurations, mixedtechnologies, fluctuating precisions, and coarse vector changes beyondpre-set specifications.

From Raw Solutions to Structured PhaseNet Solutions

For the baseline embodiment of the ten cars in the gymnasium, despitethe challenges presented above, pseudo-inverse solutions to equation (3)can be obtained on the blocks of data and the overall procedure appliedone epoch to the next, resulting in dynamic (motion) solutions of thecars relative to one another and the individual counting rate variationsrelative to one another. There are large matrices involved thatgenerally require re-inversion every few seconds because of range andcoarse direction dynamics. The matrices do require regularization intheir pseudo-inversions, which adds a small amount of overall error tothe solutions, but it works. This may even be called the raw PhaseNetsolution. It has at least organized the data collection and solutionformation in reasonably complete terms.

The central PhaseNet challenge, which this disclosure addresses andfully meets, is to turn the raw information content inherent in the datacollection procedures and the metric solutions outlined in equation (3)into universally scalable protocols, shared structures,hardware-agnostic procedures and modular algorithms. The term “plug andplay” comes to mind. From cars on a gymnasium floor using audiocommunications to space probes using laser communications, a genericPhaseNet enabled node should cooperate with any other PhaseNet node ornetwork of nodes, and together form optimal space-time solutions. Theraw mathematics falls into line once a PhaseNet foundation is in place.

To attain the necessary universality of functionality betweenapplications and physical instantiations, time-positioning approachesincluding differential GPS, WAAS, e-911 cell location, blendednavigation methods using INS and/or accelerometers, precision timing andnavigation approaches and two-way time transfer methods may be fullydescribed in PhaseNet topological terms, enhanced through PhaseNetnetworking principles, and extended by using PhaseNet shared protocols.Translating existing methods into PhaseNet architectural terms andstructures requires understanding how pings, pongs, and pungs areaccomplished in any particular prior art system, followed by howexisting algorithmic solutions relate to the predictive PhaseNetsolution families and the asymptotic PhaseNet solution families. Aproperly constructed PhaseNet architecture accommodates existingmathematical approaches. If it requires extension or modification, ithas not been designed properly.

This last point is possibly the most subtle yet important distinctionbetween PhaseNet and prior art approaches to space-time calibration:PhaseNet is designed to be a general network operating system forspecific space-time calibration approaches, including GPS, DGPS, and itsrelated systems. Productization of the space-time calculating enginefound in GPS receivers, combined with a counter, replete withgeneralized information source inputs and solution family outputs, is animportant difference between PhaseNet and existing methods. A GPSreceiver contains a sub-unit that routinely collects signal informationand cranks out space-time solutions, while a “PhaseNet Core,” be itsoftware or hardware or any combination thereof, performs the samefunction for any device that communicates (or just listens, e.g., a GPSreceiver itself). The product of PhaseNet is this universal core, oftenreferred to here in this disclosure and elsewhere as a Space-TimeCalibration Unit or SCU.

The Generic Space-Time Calibration Unit: SCU

FIG. 14 depicts an SCU at an abstracted high level. To the extentequation (3) is the most abstracted core of the mathematics of PhaseNet,designed to be independent of the supply of data through the g vector,the SCU is independent of the mathematics or algorithms chosen for itsinner workings. As it is fundamentally industrially-scalable, utilizingequation (3) as a core numerical engine is merely an option (though onealmost always chosen in practice). One could equally choose a chemicalsniffing engine (as a linq) to detect being within a few kilometers of ahog farm (ping data that feed the algorithm inside the solutionsmanager), or a wireless access point proximity engine to provide coarseproximity information to a node, as opposed to explicit metric spacesolutions. In this sense, PhaseNet is designed to be a blendedall-source space-time core, using familiar jargon in both GPS circles aswell as existing wireless device locations. The job of the SCU is togather all bits of space-time information it can find (generally managedby the data manager and the spade work done by the lower drivers), andto calculate predictive and asymptotic space-time solutions that can beshared with other nodes. The architecture it uses to do this begins withthe structures described in the next section.

With reference to FIG. 14, on the far left is a local clock and/orcounter that may or may not be a physical part of an SCU. CertainPhaseNet applications do not require a local clock or counter to bepresent, where one is essentially imputed to the Qlocknode throughshared external processes, but the baseline embodiment prefers a localcounter. On the far right is a circle representing the ultimatespace-time solution outputs that the SCU produces, along with an uppercircle labeled “GT1” standing for Global Tone 1. GT1 represents anexplicit attempt by a given Qlocknode either to steer its local clocktoward some shared network standard (which may also coincide with GPStime or UTC), or to keep track of the drift of its local clock from thatshared network standard. It is referred to as a “tone” because ofhistorical attempts to maintain certain shared frequency standards, suchas the classic 5 MHz tones typical of cesium clock network coordination,or the familiar dial tone on land telephone lines. The “tone” itself, isasymptotically defined for the past and can only be approximated in“present” realtime.

The core circle in FIG. 14 is split into the typical housekeeping andlow level activities of a generic CPU (whether dedicated to the SCU orborrowing an external CPU), and the time-critical calculations besttypified by “vector processors,” which performs highly pipelinedadditions, multiplications, and other operations on series of binaryvalues.

The outer circle surrounding the core is split into four parts. Theseouter circle sections are referred to as “managers” in that there existsa wide range of options for how to physically instantiate them. Below,the baseline embodiment implements these managers as processing loopswith associated high level structures managing the data within theloops.

Finally, the truncated second outer circle in three sections are thedrivers for collection and communication of raw and processed data,including interfaces and data exchange with instruments and systems,which are largely self-contained instruments computing their ownspace-time calibration functions, with the most common type being a GPSreceiver.

Universal PhaseNet Data Structures

The architecture of both hardware and software SCU's is built around thePhaseNet “Qlocknode” or just “Qlock” structure, or “qlockstructure.”These terms are intended to highlight participation and specificattributes within a set or group of cooperating PhaseNet nodes. The “q”on what otherwise would be “clock” highlights that the SCU/PhaseNetstructure counts like a typical clock, but it does so explicitly withina network of other such counting nodes using shared communicationprotocols.

A key engineering notion associated with the qlockstructure approach isthat an arbitrary dynamic network of disparate communicating devicesshares a common geometric and computational framework in order to beinteroperable and scalable. To be truly scalable across spatial scalesfrom intra-CPU circuitry through cis-lunar communications networks, allnodes at some level share pre-agreed structures and dynamiccommunications protocols inherent in those structures.

The following paragraphs step through the Qlock hierarchical structure,or qlockstructure: using a Matlab type formalism, the following is thetop tier organization of the Qlock structure:

% base template for all qlocks Qlocknode = struct( ‘info’, info_struct,‘data’, data_struct, ‘solutions’, solutions_struct, ‘linqs’,linqs_struct) );

To participate in a PhaseNet network, a component, device, instrument,or vehicle either instantiates this structure itself or, in the case ofcomponents or devices streamlined to collect and transmit ping data, ornon-cooperative objects such as radar targets, have this structurelocated within the network. In other words, no matter where this actualdata structure is physically located, all PhaseNet nodes have this basicstructure.

Stepping through the Qlocknode structure begins with the name itself,which in C/Matlab code instantiates a generic data structure conformingto the text fields and sub-structures inside the parentheses. Thisformalism associates four broad and multifaceted sub-structures with thetext fields just before them, each of which is described herein. Thesesub-structures are designed to continually evolve as usage modelsextend, and furthermore, it can almost be anticipated that the number ofcore sub-structures will increase beyond four once actualindustrial-scalability occurs, i.e., wide deployment of PhaseNet-enabledsystems is expected to put pressure on clarifying and expanding even thehighest level of PhaseNet organization.

Initially, the four sub-structures are: node specific information, theInfo′ text field and its associated info_struct; raw data informationthat is being collected and shared, dominated by D3P data and rolled upin the data_struct structure; all information associated with space-timesolutions or other information derived from the ‘data,’ stored in thesolutions_struct with the text field ‘solutions’; and finally the broadcategory of information associated with communication links betweennodes, network topographies, real-time cooperation tracking, and evensuch items as the status of the availability of alternate informationsources such as compass data, inertial navigation unit data and thelike, all going under the text field ‘linqs,’ indicating aPhaseNet-defined relationship between a node and the external world. The‘linqs’ field is the structure most likely to be expanded quickly intomajor sub-fields for any given PhaseNet application.

Stepping through the four substructures at the next level of granularitybegins with the info_struct structure as it exists within the baselineembodiment:

info_struct = struct( ‘static’, static_info_struct, ‘dynamic’, dynamicinfo struct );

There remains a high level split in data between node information thatremains stable through the node's lifetime and is an inherent propertyof the node, and node data that is a dynamic operational characteristicof the node and its evolving relationship to the network the mode findsitself within. This split becomes clearer by looking at the constituentsof node information:

static_info_struct = struct( ‘name’,’nullstring’,‘static_ratings’,component_ratings, ‘category’,‘robotic car’, ‘type’,‘2D-4DOF’, ‘mobility’, ‘slow moving’, ‘qlock_data’, qlock_type_and_data,‘num_comm’, num_comm_terminals, ‘num_noncomm’, num_noncomm_sources );and... dynamic_info_struct = struct( ‘current_status’,current_status,‘dynamic_ratings’, dynamic_ratings_data, ‘current_framework’,framework_rating, ‘current_harmonics’, curr_harmonics, ‘clock_steering’,clock_steering_history );

In the baseline embodiment, some of these fields are not necessarilyused, or their values are uniform across the entire set of cars. Theidea behind these structures is that the same templates are used acrossall PhaseNet applications and hardware configurations. One importanttype of data, referred to as ‘ratings,’ relates to the quality of dataand solutions associated with a given node and what it can contribute tothe network. In many ways, this concept is akin to error analysis,though in PhaseNet it becomes more explicitly part of network solutionsand the ‘weighting’ of input sources as a function of their perceivedquality (both static and dynamically quantified).

The qlock_data field in the static structure identifies precisely whattype of counting this particular node performs, including information onclocks on-board the node or accessible to the node. The qlock_data fieldalso has inherent quality information about the local clock, to whatextent precision oscillators are used by the node, and the oscillatorsrelationship to the clock and to digital counters, if applicable.

Another concept within the dynamic structure, encapsulated by astill-broad framework_rating data structure, relates to the ongoingrelationship and error estimation between a node and one or moreexternal standards to which it subscribes. A very common situation is agiven node's relationship to the GPS framework, for example, or moresimply a node's latitude, longitude, (and altitude) relative to Earthcoordinates. The specific dynamic information contained in this fieldrelates to higher level relationships between a node and these externalstandards, while real-time error estimation of the actual solutionswithin these frameworks is contained within the solutions structure.

Another data field is the ‘harmonics’-type data, which contains highlevel information regarding ping rates, solution rates, harmonic blockconfigurations and how the node organizes its data input to solutionsoutput relationships both internally and within its local neighborhood.

The clock steering field may range from a null value indicating no clocksteering is used to a very complicated sub-structure replete with a pastrecord of steering the local clock to maintain rough synchrony witheither a local network of clocks, or some external standard such as UTC(Universal Coordinated Time in its French acronym form). PhaseNet isdesigned to not require any clock steering, inasmuch as one of the mainsolved metrics is the difference between a local counter and someexternal standard such as UTC; it is often convenient to gently steerall clocks to some globally accepted standard such as UTC or GPS time(which itself is generally steered toward UTC).

With reference to the second of the four main sub-structures:

data_struct = struct( ‘pingforms’, pingform_data, ‘pongBuffer’,pong_data, ‘simData’, simulation_data % used on for simulations );data_struct is deliberately simple in the baseline embodiment, keepingthe raw data structures to an efficient minimum and being explicit aboutsome of the interim data structures in forming full harmonic blocksolutions (e.g., dpingforms, which are informationally equivalent topingforms).

The pingform_data structure contains all of the raw informationcollected by the node through listening and count-stamping ping arrivalsfrom other nodes. In the case of information sources such as, forexample, speedometers and INS information, raw data outputs from thesedevices, as count-stamped by the node's local counter, would havesimilar raw data structures. The pingform_data structure is essentiallywhat is behind the waveforms in FIG. 6. It is shown later that thesedata buffers are ring buffers that only capture recently acquired data,that is discarded once the solutions are determined.

The pongBuffer text field, together with its associated pong_data datastructure, is a communications buffer for the digital ‘back-channel’data that this given node communicates to other nodes. Though they canhave a variety of components and properties, pong data is considered‘raw data’ in that nodes require receiving these pong data before theycan form asymptotic solutions. It is thus a kind of shared raw data. Onemain property that distinguishes pongBuffer data from pingforms is thatpingform data is compressed before placing it into the pongBuffer, sothat pong communications are maximally efficient from an informationstandpoint. In general, pingforms are meant to be “oversampled” in time,meaning that ping sampling is at a rate sufficiently higher than therates of system variations being measured such as spatial movements andcounting variability. Thus a reasonable compression of data can occur byfiltering raw pingform data and using lower bandwidth waveforms (furtherapart digital value time spacings) to represent the acquired pingformswithin the pongBuffer data structures. Later sections will show how thisoperates in situ.

The ‘simData’ text field, together with its associated structure, existsonly for a car simulation and would not be used in actual operation ofthe cars in the physical world. The simData structure is meant tocontain “truth data” that would plot the courses of simulated cars, aswell as contain simulated variations in counting between cars. ThissimData could then drive mathematical models of how the cars move about,communicate, produce data, and eventually create solutions. Thesolutions would be compared against the original simData, and erroranalysis could then be performed on the raw PhaseNet operations andalgorithms. This simData structure as outlined here cannot incorporateby reference a physical demonstration.

The third of the highest level sub-structures is:

solutions_struct = struct( ‘nominal’, current_estimate,‘current_H_inverse’, current_Hinv_struct, ‘local_harmonic’,local_harmonic_solution, ‘global_harmonic’, global_harmonic_solution,‘asymptotic’, asymptotic_solution_family, ‘ratings’, self_rating_data );Though this baseline embodiment has five elements in this particularexample of the solutions_struct, there are three basic types ofsolutions: ‘nominal,’ ‘harmonic’ and ‘asymptotic,’ with an additional‘ratings’ data structure that will contain self-rating (as well aslocal-group-assisted self-rating) information on the estimated qualityand reliability of the solutions being produced by particular node.

The ‘nominal’ solution is meant to encapsulate virtually all modernstate-of-the-art methods and algorithms that attempt to calculate timeand position of an object in pre-metricized space-time systems such asGPS. In short, a given node has a given estimate of its absoluteposition at a given local moment (as defined by its clock) when it sendsout a ping, a second receiving node receives this ping at some point inabsolute space, and straightforward “pseudo-ranging” solutions areformulated. Time and position are treated as absolute quantities with anotional error metric attached to those quantities. Straightforwardgeometry, dominated by triangulation and angle measurements, drivesnominal solutions.

For many applications, including well-behaved examples of the baselineembodiment, the nominal solution is sufficient to meet given time andspace accuracy specifications, especially when one or more nodes havebeen given very high accuracy ratings, effectively becoming a “masterreference,” as is common in many existing systems. Systems wherein thenodes are closer together than one ping-time see that the nominalsolutions are often close to the more fully constructed asymptotic andharmonic solutions. The scalability of PhaseNet will put a pressure onping times to speed up over the years, thus shrinking the spatial scalesover which nominal solutions are very close to asymptotic solutions andattempting to increase the time-bandwidth with which a given node'sphase noise is being measured.

The ‘harmonic’ solution type is the raw solutions to equation (3), asregulated through pseudo-inversions stored in the H_inverse structure,and other ad hoc methods. These solutions, at least in the baselineembodiment, are in terms of variations in the system from moment tomoment, tracking spatial movements, count variations, atmosphericvariations, and instrumental delay variations. These solutions also areexplicit in dealing with mixed-epoch interaction of waveforms, in whichabsolute space-time coordinate solutions similar to the ‘nominal’solution family become quadratic formulations, which complicate theimplementation of equation (3).

The harmonic type is split into local harmonic and global harmonic. Theprimary distinction between local and global is that in the localharmonic case, a small number of nodes form local groups and produceself-consistent equations of the form of equation (3) which can besolved for very short-term variations as a small group. In the globalharmonic case, aggregations of many nodes take some time to gather pungdata for the entire set, and the resultant matrices are therefore ratherlarge. Most applications form local harmonic solutions, while higherprecision systems and “standards-oriented” systems use a global harmonicsolution approach. In the baseline embodiment of ten cars, an example ofthe difference between the local and global harmonic solutions is givenbelow.

Finally, the ‘asymptotic’ solutions are effectively the post-hocsyntheses of the nominal and harmonic solution types. To the extent GPSreceivers, speedometers, compasses, and INS data are data sources aswell, the asymptotic solutions are where the “all-source, mixed-epoch,hyper-blended solutions” are formulated. Certain existing approaches inparticular applications apply information blending principles to‘nominal’ solutions on a case-by-case basis.

PhaseNet protocols and the resulting asymptotic solutions attempt tohardwire the all-source information into the short-period harmonicblocks, thus not only including such information in the variationalharmonic solutions directly, but also helping to handle intermittentsources that are nevertheless useful during the scattered moments whenthey are providing information.

The baseline embodiment presents examples of these three basic types ofsolution, their relationship, and how different applications utilizedifferent aspects of the various solutions.

The ‘self ratings’ text field and associated data structure isstraightforward conceptually in that it attempts to associate an errorand/or accuracy estimate with one or more solution types, generally on aharmonic-block by harmonic-block level basis. It is in effect thereal-time error-bar plot of the solution(s). This data structure has animportant role, however, in that the ratings data structure is used bothas a binary gate when another node decides whether or not to includedata generated by this node, and if so, how much to rely upon and weightthe data generated by this node. The self-rating data becomes an activedata structure primarily within the asymptotic solutions, but also inthe harmonic solutions as weighting factors on the data, or even thegate on whether certain rows of the H matrix of equation (3) exist ornot.

The fourth and, for the baseline embodiment, last main sub-structuresis:

linqs_struct = struct( ‘linq_list’, current_linqs, ‘linqweights’,current_linqweights, ‘linqvectors’, current_linqvectors, ‘RRQ_table’,current_RRQ, ‘local_solution_groups’, local_group_data,‘regional_topology’, topology, ‘topology_graphic’,current_topology_graphic, ‘H_family’, H_family_struct, ‘external_frame’,reference_frame_linqs );

The linqs_struct is possibly the most important and wide rangingstructure within the PhaseNet core set of structures. It contains allthe information necessary to relate a given node to its external worldand all the space-time information sources available to the node, inreal-time and changing as the node changes according to its environment.The term ‘linqs’ encapsulates this interface between a node and allother nodes (its environment). The q in “linq” highlights itsinteraction with all other nodes or its environment with a PhaseNetprotocol: sending out pings, receiving pings, count-stamping pings andother events such as pongs or natural beacons, storing information inpung packets, and communicating those packets. Perhaps the mostimportant aspect of the linqs_struct is its ability to track andcharacterize node to external world relationships approaching ping rateand harmonic block rate bandwidths, as highlighted by the RRQ(Riccian-Raleigh-Quality) table. The RRQ table illustrates how networksof nodes handle an environment subject to echoes and jamming such as abattlefield or an urban core. This characterization of a ling or duplexlinq at the millisecond and finer level is sorted by line-of-sightversus scatter criteria and signal-to-noise ratio. The RRQ may berefined according to the adaptability of PhaseNet and its ultimatescalability to any environment.

The overall organization of the linq_struct is closer in type to hownetwork layer software keeps track of ongoing network connections andconfigurations than it is to the mathematical type of thesolutions_struct, the raw data storage type of the data_struct, or theidentity and properties description type of the info_struct. Thesedifferences in type largely drive the categorization of the four primarysub-structures.

The ‘linq_list’ structure is simply the list of both active andpotentially active sources of information to the node, while the‘linqweights’ structure represents the binary or weighted status of eachone of those listed linqs. FIGS. 3a and 3b previously described aregraphic depictions of what a typical linqweight looks like. This graphiclinqweight as depicted is a binary ‘on/off’ indicator for a ling inquestion, though this could easily be a realtime weighting value drivenby the quality and/or reliability of the linq at a given instant.

The ‘linqvector’ data structure is fundamental to forming equations likeequation (3). It contains the coarse direction vectors (the k's, inequation (2)), as well as coarse range values. The coarse range valuesassist in interpolations within the multi-epoch waveforms.

Both the linqweights and linqvector data structures are simplified formsof the RRQ table. FIG. 15 introduces on the left a kind of ‘analog’ ideabehind the RRQ table where pure Riccian channels (e.g., a line of sightcommunication, such as a laser communication channel) is at the top ofthe vertical axis and a pure Rayleigh channel (such as cosmic backgroundradiation) is at the bottom, while straightforward signal to noise ratiois the horizontal axis. The right side of the figure introduces the morepractical aspect of the table itself with definitions of the tabularrows and columns to be determined at one level as aPhaseNet-application-wide set of guidelines, but also to be refined forgiven specific implementations of PhaseNet. Notably, the Q axis can bemuch more than just signal to noise ratios, where for example a channelthat might have several echoes (delay times for the arrival of signalsin a packet-based protocol) may nevertheless have a cleanly definedgeometric property or echo. FIG. 16 attempts to show such a situation,where an obstruction is introduced between one car and another, yetthrough group cooperation, the two cars can determine that an “ad hoccoarse k vector” can still produce good information on differentialspatial movements of the two nodes. The ‘current_RRQ’ is thus astructure that holds this kind of sub-second realtime information aboutthe state of the linq and any necessity to modify k-vectors; it can alsohelp determine the weighting of a given linq in the solution roll-ups.For jammed battlefields and echo-rich urban cores, the main engineeringtasks and refinements are those inherent in the RRQ data structure andits relationship to pings and harmonic blocks.

local_solution_groups' is a higher level logic structure that attemptsto understand local relationships between communicating PhaseNet nodesand how “closed form” solutions can be formulated in tightly definedgroups of nodes. This structure contains data describing which nodes areconnected, and through what collection of pings and pung packets, canharmonic (or nominal) solutions be formed. This generic structure isdesigned to be dynamic, tracking rapid re-configurations and possiblyeven changing from harmonic block to harmonic block.Local_solution_groups also keeps track of the unique status of certainnodes in local groups that have calibrated or “server-like” properties,or even simply a stand-alone GPS receiver with rudimentary or ‘wired’(non line-of-sight) communications capabilities. ‘Local_solution_groups’is primarily aimed at supporting and recording the wide variety of localharmonic solutions that can be formed; ‘regional_topology’ is a broadtype of data structure similar to local_solutions_group, but primarilyaimed at supporting asymptotic solutions and large PhaseNet networks. Itstill is geared toward forming variation solutions of the equation (3)type, while keeping track of raw network topologies that ultimately willbe grounded in globally accepted standards and frameworks. Closelyrelated to ‘regional_topology’ is the ‘topology_graphic,’ a structurethat is an intuitive aid to programmers and PhaseNet users who wish tovisually understand the status of a given Qlocknode, its availableactive linqs, and its relationship to an external network. It isanticipated that though the topology graphic may not be of use to thedata loop operation or the solution loop operation, it will neverthelessbecome an important structure for programmers and users.

‘H_family’ is a structure that may possibly be placed inside thesolutions structure next to the H_inverse structure, but in the presentcase it is placed in the linqs structure. The constantly changingnetwork topologies and local group combinations/detailed geometry mayencourage reformulating the H matrix in equation (3), and then invertingthe reformulated matrix. Such a requirement would drastically increaseprocessing demands. Instead, closely associated with tracking currentand evolving linqs, more slowly varying ‘families’ of possible Hmatrices are tracked within the linqs structure, with particular Hinverses then removed from those families and placed into the solutionstructure for tighter-loop operations.

The last data structure named ‘external_frame’ is meant to encapsulatethe details of how a given node connects to an external space-timeframework, e.g., the GPS frame. This can be as simple as the singulardata structure, which effectively says “my close neighbor node, node Z,has a very good GPS receiver and I'm simply going to steal its values”,or as complicated as many sub-structures in a precision PhaseNet nodethat is cooperating with many other precision nodes attempting tosupport precision time transfer and data exchange between space-timecalibration sites.

The PhaseNet structures outlined above are meant to provide a functionalstarting point for the baseline embodiment as well as highlighting a fewitems that go beyond the needs of the baseline embodiment (includedbecause they are fundamental to almost all PhaseNet embodiments andapplications).

Active Versus Passive Qlockstructures

The assignment of a qlockstructure to a node is the basis for adheringto PhaseNet protocols, combined with basic software operation. Note thatPhaseNet principles can be applied to passive or non-cooperative objectsas well as active participants. FIG. 17 depicts the difference betweenactive and passive PhaseNet nodes.

FIG. 17 shows that some “passive node” object external to a set ofcooperative PhaseNet nodes can be still treated as a node throughreceipt of either reflected signals from that object, or signals emittedfrom that object itself, or both. An example of the former case is anaircraft being illuminated by radar, with several PhaseNet-cooperativeradar receivers sensing reflected signals. Examples of the former are arepetitively pinging RFID which simply sends out periodic ID messages,and EM noise emitted from a motor.

In all cases, a PhaseNet-enabled set of nodes can instantiate aQlocknode structure for the passive object, characterize the propertiesof the object by filling out appropriate fields and data structures inthe four sub-structures, and proceed to collect information as apong-type signal measurement (i.e., a ping sent out, reflected, andreceived by the pinging node). Variants to a pure pong include just onePhaseNet node sending out a ping with multiple nodes receiving thereflected ping, as well as some non-PhaseNet EM source pulsing theobject, the reflected pulse being captured by cooperative PhaseNetnodes. Count-rate variations normally associated with a PhaseNet nodecan be treated as a null parameter for purely reflective topologies.Count-rate variations can still be calculated for situations whereeither an external EM pulse is illuminating the object, or some signalemission from the object is being received and one wishes toindependently measure rate variations for what has become a virtualcounter associated with the passive node.

Passive nodes do not contribute information to solutions in the form ofnew equations of the type similar to equation (2) simply because theycontribute no data directly to the overall network. Hence, solutionsthat contain passive nodes may not be over-determined by the resultantequations, an intuitive and natural consequence. The same principles ofsolution still apply, however.

The Garden-Variety Qlock

The baseline embodiment and other PhaseNet applications have activeqlocknodes. FIG. 18 shows the most common, highest level wrapper thatusually surrounds a Qlocknode, akin to FIG. 14 describing the SCU, buthere depicted even more simply. There are three basic elements in thiswrapper: a counter (clocks, oscillators, etc.); a count-stamp enabledchannel; and a CPU that can instantiate a Qlocknode structure. If any ofthese common elements is missing from a given node, that node becomesdegenerate. There are various ways for the network to compensate formissing elements on members of the set. The channel in particular mayhave out-bound digital communications capabilities in addition to thecount-stamping capability, though certain network topologies (such asRFID) may have only incoming digital communications. The moniker“active” signifies a node that is “tied into the active functioning of aPhaseNet enabled network.”

The channel circle in FIG. 18, represents external salient communicationor data sourcing, thereby representing a generic linq that can becount-stamped. There may be zero, one, or more than one linq for eachnode, in which zero often correlates to a passive Qlocknode. Someconfigurations such as active RFIDS may have no reliable linqs that canbe count-stamped, yet they still have a digital back-channel availableas well as a counter, making then essentially an active participant in aPhaseNet local group.

The counter box in FIG. 18 simply represents any form of capability tocount-stamp, time-stamp, enumerate or otherwise label events that areoccurring on the available linqs. In PhaseNet applications, space-timesolutions are directly related to the noise level and quality of thecount-stamping capability, as opposed to the counter rate. Speed ishelpful, but it is the raw Guassian noise properties of count-stampingevents which determines the quality of the overall space-time solution.The channel circle in FIG. 18 indicates an ability to send out digitalinformation to one or more external qlocknodes.

Finally, the CPU circle is included as a typical element of a normalPhaseNet node, though it is unnecessary if other nodes control theformation of space-time solutions for a given node. For instance, a cellphone without a CPU or with a very minimally capable one may be assistedby a mainframe within the cell network that performs the PhaseNetoperation steps.

A First Sampling of PhaseNet Topologies

FIG. 19 shows three simple examples of PhaseNet topologies, illustratingthe previous discussion about active, passive and normal qlocknodes. Inall three cases, each of the filled-in circle nodes is considered aPhaseNet node. The graphic on the left indicates a passive/reflectivetopology. The graphic in the middle indicates a partly active example ofa cell phone, which has no counter but can send out regular signals thatare received by other normal PhaseNet nodes. The graphic on the rightshows a simple example of a normal Qlocknode, in this case an 802.11client directly communicating with a singular access point, but also isat the same time in duplex ping communications with a couple otheraccess points (showing that there need be only one pung channel butmultiple ping channels in operation at once), while those access pointsmay themselves be in communication with a network of nodes excluding theinitial node. These three topologies are inadequate to introduce thetopic of PhaseNet topologies, but they will suffice to show how activeand passive PhaseNet nodes can inter-operate.

PhaseNet Dynamics

PhaseNet operates on the premise that communication is ongoing betweennodes, or between a node and its environment. Given that all forms ofcommunication have some finite speed, dynamics is therefor an integralaspect of PhaseNet operation. This may appear obvious, but FIG. 20attempts to encapsulate and depict this central aspect of PhaseNetoperation.

In FIG. 20, a node designated by the letter Q undergoes three successivestates of communication with its environment. It is simply evolvingthrough arbitrary states of network connectivity, embedded in a notionalgeometry of that connectivity which is akin to the coarse directionvectors previously discussed.

Solid lines indicate functioning duplex ping channels, dashed linesindicate mono-directional ping channels, and parallel lines indicate anactive pung packet being pushed from one node to another. Though threeinstances of local group connectivity are depicted, an intuitive notionof continuous background time fluidly moving from one state to the next,replete with motions on the coarse direction vectors, would an animatedversion of FIG. 20 might look like. (Such an animated view makes sensefrom the perspective of the asymptotic background collision-frameviewpoint.)

FIG. 20 frames the basic challenges and functional requirements forPhaseNet software, hardware, firmware, and combinations of these: AQlocknode needs to determine its current, immediate past, and nearfuture network connectivity, make sure that adequate/minimal ping-dataflows occur, and calculate its own or a local-group's space-timesolutions. The following section describes the architecture and flow ofthis operational requirement.

The PhaseNet Core Engine: Hierarchical Embedded Cycles

FIG. 21 depicts the highest level organization of the operation of anSCU (Space-Time Calibration Unit), which can be packaged as a softwaremodule, a firmware module, integrated onto an IC or even a fullyself-contained box/hardware unit. The SCU engine constantly maintainsand updates the Qlock structures. Aside from typical initializationoperations, the main operation is maintaining rate-related loops wheredata flow input is managed in the data_loop, and ongoing outputsolutions are produced and broadcast to the external world by thesolutions_loop.

This manifests itself as repetitive and rate-related queries aboutwhether a given structure requires attention, and if so, providing thatattention. This highest level description will be familiar to thoseskilled in the art of real-time operating systems and associatedsoftware.

The hierarchy suggested by FIG. 21 associates the slowest loop with theinfo sub-structure and the tightest (fastest) loop with data flow andthe data structure (running at a rate related to the fastest ping rateof the particular Qlock). Each PhaseNet application will have differentdemands on the loop rates, and hence this organization is arbitrary butprobably quite close to most PhaseNet applications. Typicalloop-bandwidths are indicated. The robotic car baseline embodimentexplores various choices for these loop-bandwidths. Actual loop ratesand their relationships are fundamentally variable, and raterelationships may change from one harmonic block to the next in highlydynamic applications such as missile guidance, or may change very littlein stable applications such as inventory tracking inside a warehouse, inwhich Qlocknodes may move about swiftly, but rate relationships in theloops remain constant.

The info_loop depicted in FIG. 22 is most naturally the outer repetitiveprocess driving a given car/node's main operation, because theassociated info structure contains the most fundamental node statusinformation and provides the overall context for the node within thenetwork. For practical purposes, the linqs_loop, solutions_loop anddata_loop processes will all be “slaved” or at least referenced to theinfo_loop. The info_loop is also the place where the status andinter-relationship of the other three loops are effectively managed.Thus, the info_loop is the main PhaseNet software routine. Also notethat the “wait” condition at the end of the loop is simply a placeholdermaintaining a certain loop rate and thereby improving efficiency of theCPU and memory resources. The info_loop can contain the initializationroutines referred to in FIG. 21, including memory management and flowcontrol elements, where the very first query will find that there is nostatus and thus requires initialization. Such details should followcommon software programming practices. FIG. 22 also shows how theinfo_loop contains and manages the three other loops.

The job of the linqs_loop, illustrated in FIG. 23, is to maintain anactive understanding of the local-group topologies as depicted in FIG.19, which includes a wide variety of tasks including maintainingsufficiently precise estimates on the coarse direction vectors andcoarse range values on the individual linqs. The linqs_loop also manageslow-level details concerning quality of the linqs, intermittency, andinterfacing with digital communications sub-systems in scheduling thetransmission of pung packets. For “linqs” that are not actuallycommunications channels such as INS data inputs or speedometers, thelinqs_loop is a babysitter on the inner data_loop associated with thesetypes of linqs. The linqs_loop is also tasked with generating familiesof potential H matrices associated with both the current linqs-topologyalong with closely related topologies, inverting matrices where possibleto minimize the processing load on the solutions_loop, which actuallyperforms the ongoing solution operations. In this sense, the linqs_loopis meant to be an efficient pre-processor for the solutions_loop. Withinthe data_loop it is certainly possible to place a “break” condition ifintermittency of linqs or catastrophic breakdowns in ling active statusoccur, thus requiring attention from the linqs_loop logic on a timescalefaster than the pre-set linqs_loop_rate. The linqs_loop_rate istypically in the 10 to 100 millisecond range for most PhaseNetapplications including the baseline embodiment.

The solutions_loop is typically looped at a slower rate than thedata_loop, because it need only meet the Qlocknode's requirements togenerate a space-time solution, as opposed to the demands of thedata_loop, which is actively managing count-stamping and subsequentstorage of pings, pongs and pung packets. FIGS. 24 and 25 depict each ofthese loops accordingly. The data_loop is not generally contained withinthe solutions_loop. The two may be run as independent processes, wherethe solutions_loop depends upon interim outputs of the data_loop toformulate ongoing snapshot solutions, a relationship graphicallyindicated in FIG. 21.

With reference to FIG. 24, the solutions_loop performs many tasks aswell. The main task is to calculate and populate the types of solutionsthat the Qlocknode desires. In most cases, the solutions loop has aninverted H-matrix handy allowing a main inner loop to solve for thatvector in terms of raw ping and pung data, following the generalframework of equation (3). These raw f-vector snippets are then stitchedtogether into time series solutions using any of a variety of filteringalgorithms akin to Kalman filtering. In addition, “external referenceframe” data can be brought in to situate the variational solutions insome agreed framework such as GPS.

Other cases where intermittency of channels or extremely dynamicsituations will mean that the solutions_loop has no applicable invertedH matrix available or, if it does, that inverted matrix has gone out ofspecification ranges pre-determined by the Qlocknode. In this case, theloop is designed to either default to lower precision solutions(sometimes to the nominal solution families only), or to flag the largeloops that re-inversion is necessary to meet the performancespecifications. (It certainly can perform inversions itself, if need be,without the need to flag wider loops.) As reflected in thesolutions_struct itself, this loop may also be calculating classicnominal solutions using pre-metricized space-time. Other than CPUcapabilities, there is no limit to the number of solution families thatcan be generated at any given instance, a useful feature in volatile orjammed situations in which raw-data-supply conditions change frequently,leading to solutions that inherently adapt to those instantaneousconditions.

The solutions_loop cooperates with the linqs_loop as a preprocessor todetermine final network topography configurations and to calculate thevarious solutions.

With reference to FIG. 25, the data_loop manages the operation of D3Pdata collection and dissemination, monitors linq quality, and flags thelinqs_loop if anything is amiss. Here, the data_loop may also performraw data processing, such as bandpass filtering and data compression. Tothe extent certain solution_loop routines expect cleanly formatted andflow-optimized vector inputs, such operations may be placed in thedata_loop. And though the linqs_loop provides the interface with adevice's digital communication system to schedule the pushing of pungpackets, low level formatting and storage of those pung packets often isbest situated in the data_loop. (This reduces the number of CPU-levelregister-access calls, for instance.)

Special note should be made of the ‘switch’ statement inside thedata_loop of FIG. 25. Typically the CPU is busy executing the embeddedloops depicted in FIG. 21, with no additional capacity forcount-stamping individual ping events on individual linq-channels.Furthermore, using a CPU's internal clock as a count-stamping mechanism,of “timestamping,” is notoriously noisy and subject to long system-leveldelays and internal CPU obfuscations. A preferred approach entails theuse of a self-contained process referred to as a “ping driver,” which isdedicated to the task of count-stamping ping events on a givenindividual ling. Count-stamping may be performed by the same CPU that isdriving the main program of FIG. 21, on a different CPU or even aself-contained box.

With reference to the SCU high level operation depicted in FIG. 21, thetasking of the modules is most important in which the looping-rates andrelationships are key practical details but are also quite flexible. Theinfo_loop doubles as the main program for convenience. Certain aspectsof the linqs_loop can be implemented in common “hardware driver”packaging, familiar to engineers who deal with a variety of computerperipherals. Again, the tasks are the important element, whileimplementation is arbitrary as instrumentation permits.

Raw “f-Vector-Snippet” Solutions Using Harmonic Blocks

One approach to stitching together a sequence of f-snippets(clipped-epoch solutions to equation (3)) is to formulate equations andsolutions across two or more harmonic blocks, introducing a new harmonicblock of data and solutions each block time, to replace the harmonicblock several blocks earlier. In addition, weighting can be employedsuch that earlier harmonic blocks which already have growing estimations(solutions) due to earlier block solutions can be differentiallyweighted relative to recently added blocks. FIG. 26 shows a simplifiedexample. This multi-block approach to matrix solutions formulation ismentioned immediately in this section in order to lay a foundation foreventual Kalman-type filtering principles, which find optimal linearsolutions (and non-linear extensions) to time-evolving systems that aregenerating new data all the time.

Before placing these blocks together, however, there is a different takeon equation (3) and the harmonic block organization of the g and fvectors.

With reference to FIG. 50, a distant node B sends a ping at its countinstance b, where it has some inherent metrics associated with it atthat instant of integral count b. Likewise, node A in the foregroundlater receives the ping at its count instance a, its metrics at thefractional count instance a. There are B-specific metrics associatedwith integral count value b and A-specific metrics associated withfractional count value a. Meanwhile introducing a third shared metriccalled the common path metric(s) associated with the AB ling. Oneinteresting subtlety for other embodiments that is trivial interrestrial PhaseNet applications, is that it is difficult to associatethe ‘common’ part of common path with count values, in that a ping wassent out from A at approximately same instant that a ping was launchedfrom B, but not exactly the same instant. Practical applications retainan abstract view of the node-node ping interaction depicted in FIG. 27.

There is in FIG. 27 an arbitrary set of solution waveform points fornode B with a straight line drawn through them at a count instance b,with the line then passing downward and to the right through one or morecommon path solution points, crossing those points, and thenstraightening again as it passes through node A's solution waveformpoints at fractional count instant a. Equation (2) is a specificexample, minus the common path points. The common path metric isintroduced here because precision PhaseNet applications may use it, andbecause its introduction adds to the intuitive understanding of howPhaseNet equations are formulated. The specific graphic is from anaircraft example as indicated by the words at the left of the FIG. 27,illustrating the wide array of choices available.

FIG. 28 shows how solution waveforms based on equally spaced points canbe represented discretely through sequences of sinc functions (sin x/x).Actually utilizing sinc functions is rare; they are instead replaced bya close approximation cubic spline function. To minimize complicationsto H matrix formulations, conventional linear interpolation is performedbetween discrete wiggle-poles, thus envisioning a sequence of connectedlines as solutions for f-vector snippets.

Even the common path metric(s) use variational metrics as opposed topre-metricized absolute quantities, and hence one can interpret thewhole of FIG. 27 as the unknown metric components that give rise to asingular datum point. In this sense, a datum point is the integral of afinite number of system metrics, even as those system metrics havethemselves been discretized through FIG. 28.

FIG. 29 and FIG. 30 then take, equation (2) as a starting point fortransforming the general situation depicted in FIG. 27 and applying itto the baseline embodiment. The common path metric is not considered inthis example because it is not included in the baseline embodiment.There are six solution waveforms in FIG. 29—two dcount waveforms, andfour spatial waveforms. Three harmonic block's worth of solutionwaveforms are depicted in FIG. 29, where car B's waveforms are orderedbelow car A's, but the H row constructed will be the one in which car Areceives a ping from car B, thus rendering the dashed-line pingintegration from lower left to upper right.

FIGS. 30 and 31 relate to the principles concerning ordering of g and fvectors, taking into account not only interpolation but also harmonicblock combinations. The example in FIG. 29 is deliberately chosen toillustrate a few practical challenges as well. For example, launchingthe ping from car B did not coincide with an integral solution point andthus is a fraction within a harmonic block that, using linearinterpolation, implicates two solution points for each solutionwaveform. Car B's ping at instance b was launched in the second half ofthe harmonic block, thus requiring two ratio calculations: R_(B), theratio on the dcount waveform, and R_(B), the ratio on both spatialwaveforms. In all three waveform cases for car B, interpolationrequirements implicate solution points in the following harmonic block,designated by N+1. FIG. 30 captures this by illustrating that car B'sping includes two separate local regions of non-zero H-row elementsaccordingly.

Likewise, when car A receives a ping at instance a, it is not atintegral values of car A's solution waveform sinc pole points (or linearinterpolation pole points in this case), thus interpolation implicatingat least two solution points for each solution waveform is the rule. Inthe depicted case, the instance a is in the first half of harmonic blockN+2, and hence the implicated spatial solution waveform points are bothwithin that harmonic block, while the dcount waveform having only onepoint for each harmonic block necessarily implicates a solution point inharmonic block N+3.

FIGS. 30 and 31 appear complicated when written out in long form. Whenprogramming in C or MATLAB, using clever indexing schemes that sparsematrices (ones with lots of zeros) allow, the actual coding of FIG. 30becomes quite simple. The following is an example from athree-dimensional case:

matrixelement(index+1)=solvefrac1*kepxn;

matrixelement(index+2)=solvefrac1*kepyn;

matrixelement(index+3)=solvefrac1*kepzn;

matrixelement(index+4)=solvefrac1;

matrixelement(index+5)=solvefrac*kepxn;

matrixelement(index+6)=solvefrac*kepyn;

matrixelement(index+7)=solvefrac*kepzn;

matrixelement(index+8)=solvefrac;

matrixelement(index+9)=−frac1*kepxn;

matrixelement(index+10)=−frac1*kepyn;

matrixelement(index+11)=−frac1*kepzn;

matrixelement(index+12)=−frac1;

matrixelement(index+13)=−frac*kepxn;

matrixelement(index+14)=−frac*kepyn;

matrixelement(index+15)=−frac*kepzn;

matrixelement(index+16)=−frac;

In this example, ‘frac’ refers to fractions and the (1-frac) havealready been pre-calculated, while ‘kep’ in the longer variables hererefers to k vectors with additional ‘keplarian’ orbital elements that asatellite might need to keep track of. The code example demonstratesthat actually filling the H-row with appropriate numeric values can beaccomplished quite efficiently.

FIG. 31 then expands the view of FIG. 29 to a quasi-simultaneous duplexsequence of pings between nodes A and B. The term ‘quasi’ appliesbecause there exist slight rate variabilities between cars (which arepart of what is asymptotically being measured), so the ping instancesmay be only coarsely aligned. In other PhaseNet applications, mostnotably ones using intermittent packet-based communications such as802.11, the duplex pings may not be quasi-continuous, but may take on akind of sporadic behavior. The basic structures discussed earlier andgraphically indicated in FIGS. 150, 152 and 155 are all designed toeasily handle arbitrary ping launches and ping receipts.

FIG. 32 depicts all 10 cars operating with their pings, copiouslyproducing H-matrix rows in the process.

FIG. 33 re-packages FIG. 31 in the harmonic block organizationstructures for the g and the f vectors. One particular ping ishighlighted, illustrating how it associates two discrete f-solutionparameters for each wiggle resulting from the choice of linearinterpolation for the baseline embodiment. This number is typically fourif one uses cubic interpolation methods, or even larger if oneapproximates sinc functions more accurately with a cubic splines.

FIG. 34 then re-packages FIG. 32 into a view of 5 successive harmonicblocks, across all 10 cars and all 90 mono-directional ping channels.Also represented is the double frayed edge challenge listed earlier, inwhich pings were sent out from nodes during harmonic blocks earlier thanthe five represented, yet received during the five highlighted harmonicblocks, while other pings are sent out during the five harmonic blocksthat will not be received until later harmonic blocks. Specific examplespresented below demonstrate that this challenge can be largely′ overcomewith residual error being the consequence of never being able to fully“stop time.”

FIG. 34 inherently has 5 blocks×10 ping instances per block×90 pingchannels or 4,500 ping equations being represented, along withapproximately 5 blocks×5 f-vector unknowns per block per node×10 nodesor 250 unknowns represented. The approximation is included because, as aresult of interpolation in formulating the H-matrix, some spilloverbetween harmonic blocks is necessary. Some of the over-determination(4,500 equations with 250 unknowns) is an informational close-cousin toclassic bandpass filtering of signals that are sampled at higher ratesthan are required for eventual solutions; the same kinds of ‘averaging’S/N (signal to noise ratio) improvements are the result.

The rank-deficient (always-singular) matrix H is a manifest result ofFIG. 34. Construction of the H-matrix is demonstrated below. When the Hmatrix is inverted, variational f-vector solutions can be generated fromthe data vector g. These variational solution snippets can then beplaced into two related contexts: (1) evolving waveform estimates ofcount rate variations and spatial variations of the nodes/cars, and (2)evolving absolute metrics such as absolute distance or GPS coordinates,once asymptotic processes are applied and “speed of light” or “speed ofsound” absolute metrics are introduced (as will be shown in the baselineembodiment).

This completes the general discussion on how f-solution snippets areformulated and solved; the next section which describes how PhaseNetsoftware executes operations to feed the space-time calculation.

PhaseNet Initialization and Operation

In the baseline embodiment, a CPU manages PhaseNet data structures, theoperation of pings, pongs, and pungs, and solution formation. The CPUmay be inside an SCU, or a time-multiplexed general CPU.

Initialization of the data structures is the first order of business.The following description presents a detailed initialization scheme thatentails ten cars, and explains how the CPU then manages the data flowsof pings, pungs and evolving structures.

It was postulated above that the cars have already revved up and startedcounting. It is also assumed that before moving, each car establishesbasic audio communications with each of the other nine cars using achosen ‘audible’ or supra-audible carrier frequency, and that all 90mono-directional communication links have been established and verified.

In the baseline embodiment, PhaseNet is now ready to initialize bypopulating its linq_list data structure with the identities of the nineother cars it is communicating with, along with any specific data aboutthat car that it may require. An example of such data is the type ofclock each of the other cars carries. Data is recorded within thelinq_list structure, associated with the given node in question. Asequations are established and decisions are made about linqs,reliability, and the like, required data about other cars is added tothe linq_list data structure. The baseline embodiment stores nineletters A-J, in a given car's linq_list, while excluding its ownidentity. Each of the nine letters stores information about anothercar/node.

The cars ensure that ping data are properly being sent and receivedacross all 90 mono-directional channels. Initializing PhaseNet in thisexample entails assuming that each car has a reasonably goodclock/counter and that each car can start count-stamping every fiftiethzero crossing of its audio carrier wave. (This initialization approachis simplified; industrial applications would have more developed methodsof ensuring proper generation and receipt of ping data.) A discussionfollows of how the other nine cars determine which of the zero crossingsany other given car is using as its reference.

FIG. 35 shows how a certain car uses its counter circuitry to startcount-stamping its carrier wave. The counter and audio communicationshardware are slaved to the same clock inside the car. The trivial pingwaveform transmitted every 1,000^(th) count is shown in FIG. 36. Bymodulating a special symbol onto the audio carrier wave to indicatewhich zero crossing is considered to be the 50^(th) zero crossing,eventually another car, say car B listening to car A, begins to createpingforms based on receiving the pings from car A. FIG. 37 depicts whatthis pingform may look like.

The circuitry enabling car B to count-stamp the zero'th crossing of theincoming audio carrier is simple in this baseline embodiment, in whichsample of the received digital waveforms are slaved to the local clockand thus the sample times are known relative to the clock inside car B.Using digital signal processing, fractional sample-time values for thiszero crossing can be calculated. Thus, FIG. 37 exhibits a noisy andslowly varying waveform about a perfect line. When none of the cars isin motion, the waveform describes electronic noise and drift in the carsthemselves, with perhaps a very small amount of reflective orenvironmental audio noise contributing to the signal.

Because packets are broadcast as opposed to transmissioned between pairsof cars, only 10 separate “audio channels” are needed in the currentbaseline embodiment instead of requiring 90 distinct mono-directionalchannels to share the same audio airwaves. Thus, when a car communicatesa pung packet (as described below), it broadcasts to all other cars atonce, and the other cars broadcast their pung packets simultaneously, asspread-spectrum multiplexing of the 10 individual audio channels allows.

Thus FIG. 38 indicates the raw pingform data measured by all thestationary cars. Each car sample one single audio waveform at 100,000samples per second, fully capturing the spread spectrum channels fromthe nine other cars and count-stamping the appropriate zero-crossings ofthe carrier signals of each of the nine separate cars. These raw dataare theoretically destined for the data_struct.pingforms data structurepreviously described, but these data ought to be slightly processedbefore they are placed into this data buffer. Details of the ninechannel spread spectrum decoding are only alluded to in FIG. 38 wherethe main point is that the ‘lag,’ which is driven by a digital delay inthe spread spectrum decoding, is the entity that contains the modulationof every 50^(th) zero crossing of the carrier wave from each of theother nine cars.

The pre-processing of these raw data is very straightforward. There aretwo primary processing elements that turn the de-multiplexedzero-crossing raw data into data fit for placing into thedata_struct.pingforms buffer: (1) subtracting the “symbol modulationbias” from the zero-crossing data as previously outlined, and (2)bandpass filtering the raw data using a low pass filter with a classiccut-off around 100 Hz. This is a deliberately low bandpass cut-off forthis baseline embodiment, which focuses on 10 Hz level spatialvariations of the cars, and 50 Hz count variability levels. The 100 Hzbandpass thus preserves the information relevant to these ultimategoals.

Once these two pre-processing steps have been performed on the rawpingforms, it will be necessary to store only 200 pingform points persecond into the data_struct.pingforms data buffers, even though 1,000virtual pingform points are part of the initial sampling. Less than onesecond's worth of data requires retention until it is overwritten withmore recent data. Thus, each car collects 9×200 16-bit pingform samplesper second or 3,600 bytes of information per second for placement intothe data_struct.pingforms data Buffer. A ring buffer implements thedata_struct.pingforms buffer, such that, for example, a new block of3,600 bytes overwrites 3,600 bytes from two seconds earlier.

The data_loop procedure performs these operations just described, andthe results are placed into data_struct sub-structures as appropriate,generally into data structures that have been assigned the label of thecar from which the pings are being received. For the ‘baselineoperation’ of the baseline embodiment, this empirical data flow is themost fundamental. In a sense, each car acts as GPS satellite for all theother cars. But rather than having known orbital parameters andsynchronized clocks on such pseudo-satellites, they instead contributesent pings and collected pings that, as a combined set, create aself-consistent space-time framework.

The pingform data are also organized according to harmonic blocks. FIG.39 presents a typical organization of the stored pingforms. In thebaseline embodiment, an entire harmonic block's worth of pingform datawill be sent to the digital communications buffer of a car, to bebroadcast as a pung packet to all other cars. Cars receiving thispung-packet message (always from at least one harmonic block time in thepast) will place the received pung data into the same data_structsub-structure where the pingform data are also being stored (labeledwith the car from which the pung data were received). The pung datapacket contains nine other cars' received ping data, and thus the pungdata sub-structure is doubly labeled, first with the car from which thepung packet was received, and then with the car from which the pingformwas originally received another car. This configuration in which allcars share all information is the baseline solution example, such thatany car can create a full set-wide solution. Other embodiments maydesignate special cars that capture all the pung packet data and thushave the full information set necessary to calculate set-wide f-snippetsolutions.

The pung structure associated with one harmonic block should have 81other-node pingforms×10 pings per block×2 bytes per pingform data pointor 1,620 bytes of information if no down-sampling or compression isused. This amounts to 18,000 bytes of pung information per channel persecond, exceeding the channel capacity. Above, it was shown that only3,600 bytes of pingform data per second were stored in the pingformstructure, thus limiting the data transfer rate to 10 Kbps. One of thegoals of PhaseNet is to reduce “PhaseNet Pung Overhead,” thecommunications demand for the pung channel, to only a small percentageof a given channel's data carrying capacity. The baseline embodimentdoes not meet that goal; at the very least, the pung data rate must bebelow the channel capacity.

This is where classic data compression comes to the rescue. FIG. 40illustrates the two basic principles by which roughly a factor-of-10reduction in pung data rates can be achieved, within solution accuracyspecifications discussed later. The second derivative of the rawcount-stamped ping data is well behaved, often at or near zero, thusfacilitating compression using standard algorithms known to thoseskilled in the art. In the baseline embodiment, the pingform data arecompressed before they are placed into the pung data packet (performedwithin the data_loop). The data is later un-compressed at a receivingcar/node by its data_loop process, to be then placed into the receivingcar's double-labeled pung structure described above. Commerciallyavailable software is fully capable of serving the compressionfunctions, as most non-image based compression routinely handlesone-dimensional data sequences.

A Detailed Look at Memory Ring Buffers

PhaseNet applications, including the baseline embodiment use ringbuffers, that is, memory that has “old and fully processed data”overwritten with new data. In the baseline embodiment, a three secondrule for harmonic block data storage and ring buffer flow is used. Inbrief, an initial three seconds' worth of data is placed into memory,then the fourth second's worth and all subsequent data overwrites theharmonic block from three seconds earlier. The size of the ring bufferis an arbitrary choice driven by the fact that the solutions_loop oftenprocesses a short sequence of harmonic blocks that cross from one secondto another, and it would be preferable if neither of those seconds ofharmonic block data is overwritten during processing. A “third second”is allocated for memory swapping, while “at most” two seconds of dataare being accessed. This three second rule is deliberately simple.Experienced engineers working with much higher data rates and finertime-resolution on processing harmonic block data can push these ringbuffer requirements down to the tens of milliseconds or even smaller.

Again, for the baseline embodiment, all data_struct structures outlinedimmediately above (the pingforms and the pung structures) adhere to thisring buffer management, with the info_loop, the data_loop and thesolutions_loop ensuring that data processing is properly segregated frommemory management and the process of overwriting data.

LINQS_LOOP Operation

PhaseNet software operation was introduced by first discussing raw dataflow and data storage, but as FIG. 22, the info_loop, clearly pointsout, the data_loop and the solutions_loop are either (a) containedwithin the linqs_loop, as in FIG. 22, or (b) parallel rate-related loopsin which the solutions_loop is constantly querying the linqs_loop todetect new H-family data. Regardless of the relationship of thelinqs_loop to the other loops, it is appropriate to first discuss thelings_loop and H-family structures prior to how f-vector solutions arecalculated, since those solutions depend on H-families and H⁻¹.

As previously described, the linqs_loop performs many tasks, and thissection concentrates on how the linqs_loop manages instantaneous networktopologies, including potentially swift changes in those topologies,thereafter supplying the solutions_loop with the appropriate family ofsolution frameworks, so as to reserve the solutions_loop forcalculations rather than decision-making in highly dynamic, jammed orotherwise intermittent networks.

Referencing FIG. 23 first described in the PhaseNet dynamics section,the linqs_loop is the main process that relates a given node to itsexternal world, even if it is only one ling to a trusted source, or,even if no current linqs are available and the node can only extrapolatefrom past linqs. Recalling that a ling is any information about aQlocknode's relationship to the outside world, including INS unit dataor magnetic compass data, one can better appreciate that ‘no linqsavailable’ is a rare condition for PhaseNet applications and puts such aQlocknode into a classic “trajectory” through its best-guess of externalparametric space. For the baseline embodiment, there is brief discussionof “no linqs available” scenarios, but here it is assumed that there isat least one, if not normally many, linqs available during any givenharmonic block period. Linq configurations of interest herein are thosethat rapidly change from one harmonic block to the next, or over a fewtens of harmonic blocks, corresponding to tenth-of-a-second classchanging conditions. For PhaseNet applications, this class of conditionvolatility is the sweet spot for handling with real-world challenges andyet providing stable solutions.

With reference to FIGS. 22 and 23, the detailed steps of thelinqs_struct and the linqs-loop in the preferred embodiment aredescribed here. As part of the initialization begin in the maininfo_loop, an initialization process is initiated in the linqs_loop. Theprimary process is to inventory and set up sub-structures for linqs,including those that are not in use but are candidates to become linqs.These candidates facilitate processes in which data flow from main linqsis insufficient, and active search processes for supplementary orreplacement linqs is needed. The primary linqs inventory process alsoincludes storing, the linqs registered by a Qlocknode and, in turn,linqs for those, etc. In other words, the Qlocknode in question attemptsto understand regional network topologies as best it can, and tocommunicate with other Qlocknodes what it knows about regional topology.

Topology information is stored in the regional_topology structure.Operation of the linqs_loop includes constantly updatingregional_topology. In the baseline embodiment, for the initialconfiguration, all cars register that ten cars are part of the regionaltopology and that each car can communicate pings with all other cars.

Initialization within linqs_loop also includes estimating and storinginitial coarse direction and coarse range vectors. There are many waysto do this. The general idea is to determine coarse range to roughlyone-tenth of a ping-time as projected into audio spatial distances, andto determine coarse direction to a very coarse five or ten degrees.These specifications later determined the accuracy and residual error ofcalculations performed in the solutions_loop.

A car in the group of ten can determine coarse range to each of theother nine cars according to the following straightforward method, asdepicted in FIG. 41. A count-stamped ping is sent out by car A atcount-stamp a and received by car B at count-stamp b; car B meanwhilesent out a ping at its count-stamp b_(i) sometime just before b, andlikewise sent out a ping at count-stamp b_(i+1) just after b; car Arecords these pings from car B at a_(i) and a_(i+1) respectively, whereeach of these is a fractional count value; the value a_(roundtrip) iscalculated taking the fractional value of b about b_(i) and b_(i+1), andapplying the same fractional value between a_(i) and a_(i+1), thensubtracting the original ping count-stamp value a; the coarse range isthen a_(roundtrip) 12, in which the coarse range is calculated in unitsof car A's counts (not seconds, meters, etc.). In short, this is justthe round-trip speed of sound as measured in car A's counting system.The ‘coarse’ conversion then assumes that car A's counter iswell-calibrated to units of seconds. Then using the speed of sound inair, FIG. 41 shows the final coarse range estimate that will be storedto define the initial coarse range between two cars. Since the cars maybe moving during this initialization process, this estimate can beimproved as the solution families emerge and converge on asymptoticsolutions, thus informing and revising the coarse range estimate as thecar positions dynamically evolve.

Coarse direction can likewise be initialized and updated though thedirection calculation may be more convoluted than the coarse rangecalculation. The good news is that, relative to the coarse rangespecification of one-tenth of a ping time, the coarse direction vectorneed only be known to a few degrees or even ten degrees for manyapplications, including the baseline embodiment. The way the cars willdetermine it, presuming there are no ‘directionality’ capabilities builtinto the audio communication system, is through an initializationprocess of comparing and triangulating on the full set (45 in thebaseline embodiment) of coarse range estimates calculated above. Theprocess is straightforward: Once two cars determine their coarse range,they may introduce a third car and draw range circles around the firsttwo cars, as depicted in FIG. 42. These circles intersect at twocandidate locations for the third car, relative to the first two cars,where one location is the real one and the other is a “phantom”location. The phantom is ruled out quickly upon introducing a fourth car(not depicted), at which point all cars resolve themselves to singlethree-way intersections with rare exception. When the fourth car isadded and the first two cars perform the same two-candidate process, thetwo added cars may compare notes and resolve the two-candidateequivocation for the whole group of four, also depicted in FIG. 42. Thisprocess is repeated until all ten cars are included, and indeed,advanced methods can perform least squares fitting to the web oftriangulations, shoring up the accuracy of the resulting coarsedirection vectors.

The actual units and reference direction used by the coarse directionvectors are arbitrary, but must be specified. An external framework suchas the gymnasium floor and walls, for example, is initially ignored bythe cars. However, later, the cars may identify the locations of thewalls and then use the walls as a set-wide agreed external framework. Inthis case, one car is designated as the external frame explorer andperforms echo-location from its own location. As FIG. 43 indicates, theexplorer car finds four primary echo locations to the four surroundingwalls. Another car may repeat this procedure, and yet another. FIG. 43shows how the set may positively determine where the four walls arerelative to the group of cars, and thereafter agrees to use x and ycoordinates to store their coarse direction vectors as depicted in FIG.43.

The results of this cooperation and agreed framework is that the initialcoarse direction and coarse range data is stored in the appropriate datastructures. These structures are updated on an ongoing basis, withguidance from the solution families without repeating the initializationprocesses. However, processes are also established to ensure that thesolution families adhere to the chosen external framework, not only toensure accuracy in the coarse data structures, but also to ensure thePhaseNet space-time solutions remain fully compatible with classicpositioning systems such as GPS.

H-Family Creation Within the LINQS_LOOP

The linqs_loop then proceeds to create the H-families and certain Hinversions that the solutions_loop will make use of. The H_family_structis the ultimate destination for the results of this aspect of thelinqs_loop. The high level explanation of H-families was alreadydiscussed, and here are presented a few more details.

“H_family” refers to all mathematical relationships between acquireddata and space-time solutions. GPS uses an N×4 matrix, where N is thenumber of actively received satellite signals; this matrix and itsinversion belong as members of the H_family_struct, to the extent GPS issubsumed or utilized by any PhaseNet node. Just as shepherds whistle todogs, not simply to convey instructions (round 'em up) but to imparttiming and positioning information between whistles; Qlocks, by way ofdescriptions of the relationships between specific whistles heard (thedata) and by way of actions taken in a spatial and timing sense (thesolutions) belong as members of the H_family_struct.

This analogy emphasizes that equation (3) and its H element areinterpreted as broadly as possible. The idea here is that PhaseNetprotocols incorporate and support past, present and future methods ofcalculating time and position. This compatibility allows PhaseNet to beuniversally scalable not simply across small to vast spatial scales, butalso across all industrial and scientific applications as well. ThePhaseNet platform is flawed without this aspect. Thus, the H_Familylabel and its associated structure may potentially become a verycomplicated repository for signaling and numeric methods associated witha Qlock. Applications-level software engineers need only know how torelate vectors using equation (3); the H_family class of structures andfunctions tracks the details as directed by a Qlock.

This flexibility regarding contents of the H_family_struct does notimply that there is no preference from the start. A preferred embodimentof PhaseNet explicitly chooses the ‘multiple-epoch waveform equations’or MEWEs as the default and recommended primary relationship betweendata and solutions, and hence the normal and default member of theH_family_struct.

These MEWEs are laid out conceptually in FIGS. 44-48. FIG. 44 formallyintroduces a MEWE, and FIG. 45 lays out a specific harmonic-blockformulation for it. The interpolation may “spill over” into adjacentharmonic blocks, though this is a fairly straightforward programmingissue to deal with. FIG. 44 shows the explicit parametric forms of theequations, while FIG. 45 gives the intuitive view. The waveforms in FIG.45 are depicted as generic wiggles that represent the trajectories of acar and its clock/count drift. The waveforms can also represent thecar's orientation, electronic delays, or a parametric waveform that mayplay a role in affecting the ‘darrival’ raw data.

FIG. 46 then introduces two very important sub-classes of MEWEs that arefamiliar to both GPS cellular/wireless positioning system engineers.FIG. 46 depicts the (non-pejorative) degenerate MEWE cases of (a) GPSand related systems, where the past-epoch sending of a ping from asatellite becomes a “correction factor” in an otherwisesingle-notional-epoch N×4 matrix formulation; and (b) localizedcellular/wireless networks, wherein the transmission of electromagneticwaves is effectively instantaneous relative to the timescale of spatialmovements being measured. In both degenerate cases, geometric solutionscan easily be formed using classic triangulation methods andpseudo-ranging. The difference between full MEWEs and degenerate MEWEsboils down to two aspects: (1) the use of continuous waveforms asopposed to “point in time” methods in the degenerate cases; and (2) thea posteri versus a priori introduction of an explicit metric space.

The reason the two degenerate cases are immediately introduced alongwith the formal definition of MEWEs is to emphasize that even MEWEs arerather broad in the assumptions, approximations and constraints that canpotentially be applied to them. A “PhaseNet-wrapper description” of mostprior art space-time measurement systems is accommodated by the basicPhaseNet protocols and architecture.

Later sections give examples of the full MEWE approach for theaudio-based cars, followed by replacing the audio with EM-based 802.11where it is shown that degenerate MEWEs can perform quite well ingymnasium-scale applications for position measurement (though they beginto break down for higher bandwidth phase-noise measurement). Measuringhigher phase noise bandwidth within a group of nodes is a factor behindusing full MEWEs versus degenerate MEWEs.

Before moving on to specific examples of H_family creation, however, itis advantageous to discuss the two discrete axes of H_family members,depicted in FIG. 47. The horizontal axis has already been described: thevast range of relationships between raw data, vector g, and solutions,vector f, with the favorite and default child being MEWEs. The verticalaxis will be summarized here, with a few specific examples laterdescribed.

The vertical axis is the “Predictive and Intermittent” distinctionbetween H_family members, a key pragmatic aspect of how PhaseNetoperates. The PhaseNet architecture is designed to operate inincreasingly dynamic, harsh conditions. The structures employed must becapable of evolving to more dynamic applications, and to moreintermittent channels and data sources in the millisecond range. Thismay not be required for audio-cars not to collide in the gymnasium, butit can become important in life safety applications such as collisionavoidance in urban canyons or on the open road. The ‘predictive’ aspectof H_family structures goes to the heart of this pragmatic goal.

The simple way it does this is by not just calculating one single Hmatrix or one single H-relationship between g's and f's, but a range ofH's that may apply within the next few seconds to minutes of operation.The range can include statistics on whether a given channel will beavailable during a specific harmonic block period (10 milliseconds inthe baseline embodiment). This is particularly important in packet-basedcommunication systems such as 802.11 in which a given channel may beinoperative for seconds or more, at least from a ping-sending if notping-receiving point of view. The range may also include predictivedynamics in the local group, in which coarse range and coarse directionvectors move through their tolerance ranges on sub-second timescales,indicating slow evolution of the H structure in relation to the harmonicblock period.

The predictive axis of the H_family structure is primarily a real-timeefficiency mechanism that takes into account what kind of CPU resourcesare available to a Qlock, how to best utilize register-level circuitrywithin the CPUs and how to properly schedule H inversion calculations intime for the solutions_loop computes f solution snippets as simplevector processing steps. While the predictive axis of the H_familystructure is not absolutely necessary to PhaseNet operations, it iscentral to PhaseNet's application in harsh, jammed and intermittentenvironments. The baseline embodiment of the cars illustrates somerudimentary examples of H_family members within the two axes of FIG. 47.

FIG. 48 shows an examination of three ‘type’ families of solutions inthe baseline embodiment, along with ‘predictive’ variants on two of thetypes because the third type does not entail much prediction andadvanced calculation. This range of H-family examples is sufficient toillustrate the software-function aspects of the H_family_struct as wellas presaging a more detailed discussion about solution types and thesolution structures.

The initial horizontal position (vertical column) on the bottom of FIG.48 is naturally the MEWE-type of H solution, with the current state ofthe network as the top example, followed by some example predictivevariants based on the current state. There is also a ‘nominal’ H type,which will use quite classic triangulation methods in forming solutions,as well as an asymptotic type H structure. These H-types correspond tothe three basic classifications of the solution types.

Recalling FIG. 5 and imagining a top-view of the starting positions ofthe cars (assuming that the cars have performed the coarse range andcoarse direction operations as previously described), followed by FIG.49, which is a graphic depiction of the pings received during a singleharmonic block period for this specific configuration. This FIG. 49describes how H matrix rows are populated numerically, using a “whatdata have I received during this specific harmonic block” approach:pings sent out during this harmonic block may or may not have beenreceived during this period, while some pings received during thisperiod were launched during a previous harmonic block. Thesolutions_loop discussion examines the details of this “double frayededge challenge,” while this section simply highlights the raw harmonicblock's structure.

The particular example of FIG. 49 reveals that the formation of one rawharmonic block gives M pings being received and N parametric solutionpoints. The M/N ratio, in this case X, is much smaller than theover-sampling ratios (indicating an expectation of Y discussed earlier)because of interpolations in the parametric solution waveforms and thedouble frayed edge issues. Quick approaches outlined in the next fewparagraphs will produce a ratio closer to the expected values, andasymptotic solutions do in fact converge on the ratio Y. FIG. 49'srepresentation does contain all 10 cars' worth of ping data and all tencars' parametric solutions. This implies that each car will need toreceive all appropriate pung data from the other nine cars before the gvector is complete and can give rise to an f-vector snippet solution.

One option not taken in the baseline embodiment is to simply invert theimplied H matrix of FIG. 49. Significant regularization is required ininverting a single harmonic block (except in the PhaseNet case where theharmonic block is relatively long and the time-scales of the local groupminimize or even practically eliminate the double frayed edgechallenge). This inversion would be an H-family composed of one member.

A preferred approach is to string together a few harmonic blocks into asingle H and its inverse that represented the optimal current state ofthe system, then to consider likely variants around that optimal state.The fate of the single optimal H's inverse will be placement into thecurrent_Hinv_struct structure of the solutions_loop as, usually, themost likely vector processing array within the solutions_loop (the mostlikely H-family member).

FIG. 13 (which is drawn more abstractly in FIG. 50) depicts two harmonicblocks strung together along with ‘clamps’ on both edges. FIG. 50 showsits associated H-matrix on the left, and the inversion of the H-matrixon the right, where the inversion entails regularization methods alludedto in FIG. 50. The inverted H-matrix depicted on the right is thecurrent_Hinv_struct for the baseline embodiment. It is calculated in thelinqs_loop and placed into the solutions_loop sub-structure, themost-used structure in the solutions_loop. In the baseline embodiment,this encapsulates the relationship between raw input data, pings, andraw position and timing variation output.

FIG. 51 sketches two examples of variants on the nominal invertedH-matrix. FIG. 51 shows how a small sub-routine (that one practiced inthe art of basic dynamical systems can readily create) predicts that themotions of the cars will bring them into an “out-of-specification”condition in approximately two seconds from the current epoch—whereenough of the direction vectors k will have changed by 10 to 15degrees—and the new predicted direction vectors are already beingprocessed by the linqs_loop, loaded into a predictive H-familystructure, and possibly even inverted, if the confidence on theprediction is sufficiently high and CPU usage can justify it. Likewise,a simpler example is presented in FIG. 52, where, for example, it isexpected that the line-of-site audio communications path between car Cand car F is partly occluded by car B over a roughly half-second period,and bi-directional data in the C-F channels may become suspect duringthis period. An H-matrix and associated H-inverse are thus calculatedunder the assumption that certain two to three harmonic block stringswill ignore the C-F channels as determined on a block-by-block basis bythe solutions_loop, and therefore the alternate or predictive H-matrixthus calculated will be performed with the C-F channels completelyremoved. Those practiced in the art of dynamic systems programming willappreciate that this description of the lings_loop is necessarily briefand that there exists a wide variety of options available toimplementers of these kinds of PhaseNet systems. Nevertheless, in FIGS.51 and 52 and their associated discussion make readily apparent howPhaseNet can adapt to network configurations at the harmonic-blocktimescale.

The Solutions_Loop: Weaving f-Vector Snippet Solutions intoError-Estimated Solution Families

The method by which the solutions_loop performs these collectiveoperations at the component level has already been laid out in previoussections, with explanations of specific data structures and explanationsof the info_loop, data_loop and lings_loop operations.

It has been shown how ping data and pung data accumulate throughdata_loop processes and are available to the solutions_loop. It has alsobeen shown how the lings_loop keeps track of harmonic-block-levelnetwork topologies. The discussion now turns to how f-vector snippetsolutions are formed in the baseline embodiment, and how these snippetscan be strung together into error-bar encapsulated continuous space-timesolutions for a given Qlock, its relationship to neighboring Qlocks, andtheir collective placement into one or more external frames.

For example, f-snippet solutions have already been described withassociated discussions on how snippets can be strung together in apseudo Kalman-type fashion. This section emphasizes on software-hardwareprocesses inherent in the solutions_loop as a defined entity. The job ofthis entity is to compute space-time solutions and their associatederror estimates. There exists a myriad of options available toimplementers for specific algorithms and accommodating families ofalgorithms and solutions. The solutions_loop may be seen as a vesiclefor a range of evolving algorithms, with an initial high levelorganization between classic triangulation-based solutions that ignoresspeed-of-communications delays between nodes or treat them as post-hoccorrections, interim harmonic solutions that isolate group-levelintervariability, and the more scalable and robust MEWE-derivedasymptotic solutions and the unavoidable reality that realtime datacollection systems of any kind can only asymptotically define pastmetricized states.

Node-Centric, Local Network Space-Time Solutions

The most general form of PhaseNet solutions entails that a single nodeshould determine its own space-time solutions based on neighboring nodesit is in communications with, where the notion of the linq extends thissomewhat by treating internal instrumentation as linqs to some form ofexternal node. This property of a single node is fundamental to thescalability and network robustness of PhaseNet.

As with all aspects of PhaseNet, the choices and details of preciselyhow to do this are deliberately and maximally flexible and adaptable toboth application directives as well as improvements in technology andinformation sciences. This flexibility is built into the protocols andstructures utilized to formulate ongoing matrix solutions and governsthe time-dependent evolution of the matrices involved.

Perhaps the simplest form of this node-centric point of view is acombination of external de facto trust combined with a classicpre-defined metric space that forms the mathematical basis to expresstrust. In other words, a given node simply accepts the estimate of aneighboring node in that it was definitively at location x_(i), y_(i)when it sent out ping; at globally synchronized (coordinate) time t_(i),the same given node trusts its own clock in that it received ping; attime t_(j), the given node collects such pings from several neighborsaccordingly, and determines its location based on classic range circlesas indicated in FIG. 53. For pings that the given node sends out veryclose in time to t_(j), the node will report this spatial estimate toits own neighboring nodes that in turn those nodes will rely on. Thisessential concept lies behind the ‘nominal’ solution family and, givingcredit where credit is definitely due, nominal solutions can becomeextremely refined and accurate especially as set within a relativisticreference metric as with GPS, GLONASS and the upcoming Galileotime/positioning satellite constellations.

Likewise, perhaps the most general form of this node-centric point ofview is that a node's solution family exists within embedded and (usingcoarse-metric definitions) spherically expanding clouds of set-widenodes, where quicker error-prone solutions can be initially formedrelying only on local sets of nodes, while eventual global set-wideasymptotic solutions can eventually be formed for any given node asset-wide pung information is globally shared and made available to thenode in question. Fortunately, this most general form of node-centricsolution can be kept as an exercise in reference frame creation andoperation.

The solutions_loop independently quantifies its own linqs, relying uponimplied network topologies both local and global, incorporatespung-channel data into its own solution framework, treats nominal versusharmonic versus asymptotic solutions relative to the specified needs ofits host instrument or device (the car), and how it presents (andrepresents) its solutions to other Qlocknodes.

Pre-Defined Metric Framework Solutions: The Nominal Solution Family

Referring to FIG. 53, skilled persons can appreciate the simplicityinherent in non-relativistic formulations of nominal space-timesolutions. Adding relativity to the party provides critical correctionfactors when viewed at global spatial scales and nanosecond time scales,but the underlying location solution concepts remain generally the same.

In GPS nominal solution approaches, clock-drift on client receivingdevices becomes a central parameter in the known and anticipateddeviations from geometric ideals, and thus there are more circlesdepicted in FIG. 53 than straightforward geometric triangulation wouldrequire, such that the additional circle (through linear algebraiccombination) allows solution for client clock drift as well.

The strength of this “pre-defined metric” approach is that it followstime-honored standard ideas. It works quite well for many applicationsin which meter-level spatial accuracies suffice or nanosecond-level timeaccuracies. The weakness of this method for use in PhaseNet-basednetworks is that it encompasses a whole host of “ultimate standards”questions (e.g., ‘who knows where who is and what gives them thatauthority?’), that questions do not affect PhaseNet's basic operationand tend to be responsible for at least some of the inaccuracy insolutions. PhaseNet harmonic and asymptotic solutions focus on theinteractions and inter-relationships between elements in sets andsubsets. Whereas nominal solutions attempt to account for errors inexternal-trust-based frameworks, harmonic solutions pro-actively try tomeasure the variational in the error. With harmonic solutions, it is asthough corrections to the official ephemeredes of the GPS satellitesthat were updated every hour or minute are now updated everymillisecond, not just because there are more efficient communicationprocesses in place, but because the GPS satellites themselves canactively participate in group-harmonic solutions.

FIG. 54 depicts an explicit nominal solution approach for the baselineembodiment of the ten cars. For simplicity, two north-south audioreference beacons are placed at opposite sides of the gymnasium toinitialize a set-wide agreement on an external framework, also servingto ground the X-Y grid system of the gymnasium floor. The beacons couldestablish a standard for set-wide timekeeping by transmitting cleanuniform timing signals (beeps), that cars could either directly use asdelay-adjusted time or as a master timing signal against which the carsmay check their own clocks. In most aspects, this nominal solutionenables the baseline embodiment to function at a rudimentary level.Using bar plots alongside nominal solutions is also a straightforwardalgorithmic approach, with FIG. 55 depicting the resultant outputwaveforms that would be placed into the solutions_struct.nominal datastructure, and the error bars would either be explicitly stored in theratings structure or some coarse general value of the error bars wouldbe stored there. ‘Nominal’ solutions thus enable PhaseNet, albeit withknown limitations that the harmonic and asymptotic solutions attempt toaddress.

The Harmonic Solutions Family

Harmonic solutions get their name ‘harmonic’ from the utilization ofharmonic blocks as a practical method to construct equations withinsets/groups that either or both exhibit: (a) unknown interactingwaveforms with inherently different underlying time constants (bandpasscharacteristics); and (b) non-trivial and essentially arbitrary delaysbetween the waveforms of one node and another node. Said another way,the harmonic block approach replaces classic discrete-time interactiondescriptions with short-snippet harmonic waveforms that retain delaycharacteristics according to the coarse direction vectors and coarseranging. A huge motivating factor in the choice of using harmonicblocks, beyond the added fidelity afforded in handling mixedtime-constant parameters, was the simplification of H-matrix row andcolumn logistics that otherwise becoming unwieldy in discrete-timeformulations for relatively dynamic systems and/or local solutionsinvolving more than a few nodes.

The formal use of the term ‘harmonic solution’ in PhaseNet expandsbeyond its generative aspect of using harmonic block waveforms by alsoencompassing the purely variational nature of both input data andsolution output. In simpler terms, the inputs are variations/changes insome otherwise intuitively defined metric, as well as the outputs beingthe same kind of changes or variations. As mentioned earlier, a ‘dx’ isjust as legitimate as an ‘x’ as an element of the f-vector solutionbeing calculated. One of the great benefits of using a variationalfoundation for the harmonic solutions, in conjunction with theapproximation represented by the coarse direction and coarse rangevariables, is the construction of purely linear H matrix formulationsfor the interactions of arbitrary sets of dynamic nodes.

Raw algorithms used to produce the harmonic solutions are, subject tochange, hence earning the title of ‘families.’ The basic solution methodhas already been laid out in earlier discussions of equation (3) and theassociated H-matrix and f/g vectors. Taking into account the genericneed for regularization because of the “double frayed edge” issues andthe inherent singularities in variational formulations, harmonicsolutions simply represent repeatedly solving equation (3). Streams ofg-vector values pour in and f-vector solutions come out the other sideof the vector processing core:

f _(i) =H _(i) ^(˜1) g _(i) ,i=1,2,3, . . .  (4)

A given node defines its own version of a local group of cooperativenodes (which could include GPS satellites as part of the ‘local group’)defining the structure of the f and g vectors themselves (and, byextension, the structure of H). Thus, a node actively solves for itsneighbors' f-vector solutions as well as its own, and likewise,neighboring nodes solve for the given node's f-vector simultaneously.Obviously if a local group is strictly defined as in the case of the tencars, and an identical stream of equation (4)'s are created that applyto the whole local group, only one node needs to actually performf-vector vector processing for the whole group and then it can simplybroadcast solutions via the pung channel(s). The potential fortime-multiplexed CPU and vector-processing loading across nodes ismanifest, if all computing resources were kept fully busy, anticipatingvarious network topologies, pre-computing H inverses continuously, thuscreating a low maintenance f-vector solution engine.

Error-mitigation in loosely connected overlapping sub-sets of localgroups is summarily depicted in FIG. 56. The possibilities forimplementation are familiar to those practiced in the art of distributedsensing and hierarchical group mensuration, extending well beyond thebaseline embodiment of the ten cars. Iterations of equation (4)solutions can be performed when disparate yet overlapping local groupsbring bias or regularization information to bear on the matrix H, andits inverse. In words, a local group over here with a few node membersalso belonging to a local group over there can bias solutions of thewaveforms of those shared members based on the initial results ofapplying equation (4) within the initially isolated local groups. Inpseudo equation form:

f _(i) _(_) ₁ =H _(i) _(_) ₁ ^(˜1) g _(i) _(_) ₁ ,i_1=1,2,3, . . . ;

f _(i) _(_) ₂ =H _(i) _(_) ₂ ^(˜1) g _(i) _(_) ₂ ,i_2=1,2,3, . . . ;

H _(i) _(_) _(1,2) ^(˜1) =f(H _(i) _(_) ₁ ^(˜1) ,f _(i) _(_) ₂);

H _(i) _(_) _(2,2) ^(˜1) =f(H _(i) _(_) ₂ ^(˜1) ,f _(i) _(_) ₁);

f _(i) _(_) _(1,2) =H _(i) _(_) _(1,2) ^(˜1) g _(i) _(_) ₁ ,i_1=1,2,3, .. . ;

f _(i) _(_) _(2,2) =H _(i) _(_) _(2,2) ^(˜1) g _(i) _(_) ₂ ,i_2=1,2,3, .. . ;  (5)

The initial solution from local group one, which may have one or moregroup members shared with local group two, can use the f-vectorsolutions it has to reformulate the H matrix (and its inverse) for group2, and group 2 can thus perform a second iteration of solving for itsown f vector using this additional information. Doing the same operationfor local group two is also possible, hence the six total equations in‘equations (5).’

One of the primary solutions_loop operations associated with calculatingf vector harmonic solutions is to ensure that the proper H⁻¹ matrix isbeing used in the vector processing step that actually calculates the fvector. The linqs_loop monitors the general current epoch andanticipated near-future epoch topologies as well as trying to anticipateintermittent ping channels, producing a small family of ‘potential Hinverse’ candidates for the solutions_loop to use, but it is thesolutions_loop that makes the final call on which exact H⁻¹ to use forany given harmonic-block epoch (or short string of harmonic blocks).This gets to the heart of PhaseNet's realtime adaptability to deal withharsh and ever-changing network conditions in the hundredth of a secondto hundredth of a second timescale: the harmonic solutions vector enginecan constantly swap in and out H⁻¹ from a small family of likelymatrices associated with the current epoch, and if an H⁻¹ is requiredthat is not part of the pre-calculated family, the solutions_loop mustflag the linqs_loop that a proper 1-1 ⁻¹ was not available for a givenepoch and that a custom H⁻¹ must be calculated for that epoch andsupplied to the solutions_loop before it can proceed with a solution forthe f-vector.

For the baseline embodiment of the ten cars, the harmonic solutionsportion of the solutions_loop includes the steps described in thepreceding paragraphs. For applications such as multi-aperture coherentdata collection wherein differential drift between collection points issufficient to “phase up” data collection, the harmonic solutiongenerally suffices and there is little need to place solutions back intoan external framework. For many other applications, however, there iseither a general desire or critical demand to package harmonic solutionsback into external metric frameworks. The experienced reader mayrecognize that this operation should essentially be an integrationactivity turning variations into absolute metric entities. Integrationconstants and “random walk” divergence of solutions are addressed in theprocess of obtaining an asymptotic solution.

The Asymptotic Solutions Family

It would not be entirely unfair to view the asymptotic family as simplythe integration of the harmonic family, or as an independent peer to thetwo previously described solution families. Though the strictest‘observables only’ mathematical formalism will always find the harmonicfamily of solutions to be on more solid ground, special classes of nodescannot be easily defined that form the basis of the intuitive world ofthree-dimensional space and commonly understood time. The gravitationalaccumulation of nodes into semi-rigid structures is the historicalprecedent here: the Earth-centered earth-fixed coordinate systems.Consideration of ‘semi-rigid’ structures is akin to the previouslyintroduced concepts of a coarse direction vector and coarse rangevariable, concepts that are central to the practical implementation ofthe harmonic solutions themselves. In any event, an objective is tomaintain a level of rigor in defining core differences between solutionfamilies, where the accumulation of nodes into semi-rigid structuresbridges the intuitive transom between the harmonic solutions world and aworld using absolute metrics and defined standards such as UTC, ITRF,ICRF, and GPS (formally defined).

In the baseline embodiment of ten cars, while the nominal solutionssub-routine of the solutions_loop is providing coarse estimates of acar's location within the gymnasium using classic triangulation methods,and the f-vectors are being produced from the harmonic solutionssub-routine of the solutions_loop letting cars know in some detail howthey are moving relative to one another, one of the cars may ask thequestion: ‘so where am I really?’ This is the job of the asymptoticsolutions family to answer. Asymptotic solutions have emphasized theword ‘family’ over the member term ‘algorithm.’

The ‘background collision frame’ or ‘asymptotic collision frame’ are twophrases that the set physics materials describe wherein the very messyproblem of rotationally interacting relativistic frames can be recastinto a functional (and conventions-defined) absolute space-time. The setphysics materials go to lengths to dissuade readers from consideringthis absolute space-time framework as anything philosophical or in somesense ‘real,’ it is simply a shared space-time metric wherein after someinformation is gathered by many nodes over a long time period, aglobally defined (shared) space-time asymptotically aligns wherein twootherwise disconnected relativistic frames can describe collisions andother events in their past. A fascinating implication of such anasymptotic frame is the possibility of exploring black hole physicsthrough an overlapping neighboring local-groups approach as opposed to apre-metricized relativistic approach that produces physicallyunrealistic singularities. FIG. 57 depicts an Earth-like planet in theneighborhood of a low mass black hole, sharing with it a commonasymptotic collision frame.

In the baseline embodiment, simpler questions can be asked, such as howcan the best of both worlds—the elegance of the nominal solutions andthe detail of the harmonic solutions—be obtained? The following outlinesa fairly straightforward hybrid choice.

Recalling the harmonic solutions derived from equation (2), wherein thecoarse direction vector was introduced, and the coarse range thataccounts for finite delays between nodes, a question arises as towhether these coarse entities can be made more accurate as more pingdata is collected across an entire set. The short answer is yes:collecting more and more data may determine what the coarse directionvectors and coarse ranges ‘were’ in the past.

FIG. 58 attempts to represent graphically the problem at hand. On theleft-hand side of FIG. 58 all ten cars are stationary so that the onlyasymptotic process required is the most simple exercise oftime-averaging coarse ranging measurements, leaving only instrumentalbias as the residual error in assigning absolute distances between thecars (and the beacons that represent the gymnasium floor, if desired).The right-hand side of FIG. 58 shows the more general PhaseNet conditionwhere cars/nodes are continuously in motion and absolute ranging is notlimited to instrumental bias.

The right hand side of FIG. 58 contains parallel node-node situations,depicted in FIG. 59, which may be described as follows: Car A sends outa ping, and continues moving along its circuitous path, while itsinternal clock drifts. Car B senses the ping sent by car A, as car Bproceeds along its twisted path, caught at some random drift of its owninternal clock; car B records receipt of the ping using its own counterand notes its fraction between the pings it has sent out, bothimmediately prior to receipt of car A's ping and immediately afterreceipt; car B of course moves and its clock drifts between its ownpings, but classic Nyquist sampling stipulates that the changes inposition measured will be only those occurring slower than the pingrates; if car A and car B are a significant fraction of a ping-lengthaway from each other, then all the dynamic twists and clock driftsbecome appreciable; by the time car A hears the two pings from car B,which sandwiched the receipt of car A's ping by car B, much variationhas occurred along with phase noise waveforms on both car A and car B.It would seem that such a turbulent collection of nodes can never betamed into an asymptotic collision frame.

Topologically Defined Metric Frames

A given topology of a set or subset of PhaseNet nodes can form thedefinitional basis for a metric space. Nodes in this definitional setare referred to as metric nodes. An ideal topology of metric nodes wouldbe stable and node-node connections would be constant, whereas realimplementations of metric nodes are highly redundant and expectoccasional minor breakdowns in connections. The word topology is usedhere in its classic form: the description of connectivity andrelationships in and amongst elements in a set.

FIG. 60 illustrates two alternate examples of topologically definedmetric spaces for the baseline embodiment of ten cars. On the left ofFIG. 60, two static beacons have been added as in FIG. 54, these twobeacons acting as metric nodes. On the right there are no beacons butthe cars are capable of echo-locating walls; car A is designated asmaster timekeeper for the group of ten cars and the four outside wallsbecome passive PhaseNet metric nodes that are being determined by thecars. Likewise, the GPS satellite constellation and the periodiccorrection of ephemeredes for each satellite are a topologically definedmetric frame. Establishing a metric frame through node connectivitycontinues to reinforce that metric frames are created throughinteraction as opposed to pre-existing.

Asymptotic Solution Example

FIG. 59 isolates two cars in the ten car baseline embodiment, depictingan arbitrary window in coarse network time where the two cars exchangepings. A return ping is shown that is very similar to the previouslydescribed coarse ranging exercise. FIG. 59 shows that the coarse rangingmeasurement alone, without additional information, cannot account forsubsequent motion of the cars during the coarse ranging operation, andhence its result strictly applied to a metric space solution will be inerror due to car motion.

In summary, the asymptotic solution family approaches must take intoaccount these motions, while continuously re-examining coarse range andcoarse direction estimates and the effect of noise on coarse rangingmeasurements and the application of harmonic solution data in theasymptotic solution formulation. Low bandwidth (classically ‘fixed’)delays in the instrumentation utilized by all cars is a non-trivialnuisance.

Of the many choices for algorithms that can handle all of these motionissues, classic noise, and delays, FIG. 61 illustrates the preferredchoice for the baseline embodiment and most PhaseNet electromagneticapplications. Other choices use classic triangulation formulae withappropriate ‘post-corrections,’ and from an accuracy standpoint, most ofthese approaches can converge to the same information asymptotes as theone chosen here that utilizes the harmonic solutions family.

FIG. 61 depicts a situation reminiscent of the classic triangulationgeometry of FIG. 54, but rather than positing cars as points withcircles around them, allowing straightforward trigonometry to locateintersection points of these circles, instead the cars are representedby wiggles indicating the harmonically measured motion of the cars overthe back-and-forth time (coarse) for a ping to go out from car A andreturn with a ping from car B. In other words, the motion of the twocars here isolated is explicitly represented, as supplied by theharmonic solutions family. Since the availability of a harmonic solutionrequires several exchanges of ping data, it can be readily seen thatformulation of the asymptotic solutions family will be delayed longerthan the delay from the basic harmonic solution.

The inset to FIG. 61 attempts to illustrate the practical considerationthat given that initial coarse range and coarse direction solutions inthe nominal solutions family are also available, a well defined range ofexpected asymptotic solutions are in a tight neighborhood around theavailable nominal solution (except in extremely high dynamic situationsor instrumentation subject to significant phase noise). The inset alsoattempts to show that there are effectively only two ‘unknowns’ in thethus-refined pseudo-ranging operation, with two inherent pseudo-rangingpings being matched to the two unknowns. These unknowns correspond tothe integration constants in the differential formulation of theharmonic solutions. When a web of cars is considered (not just twoisolated cars), these unknowns become over-determined as the number ofnetworking connections grows. It is not too surprising that theasymptotic solution family that is attempting to ‘fix’ absolute positionagainst a globally defined time also has the luxury ofover-determination.

Equations (6), follow the simplified two-car metric outlined in theinset of FIG. 61, where one car is arbitrarily set to the origin at thelaunch of the initial ping. Time is also set to zero, for simplicity,and the interim ping time ‘1’ sent from car 2 back to car 1 is notincluded in the actual equations since fractional ping values are usedat the ‘far node’ such that the raw ranging data value is measured atthe launch of a ping and the receipt of a virtual ping back at thecar/node that launched the ping. When a web of multiple cars generatesets of equations, it is a standard exercise to sort out the offsetsbetween the cars:

rangepings1=(count1_2−count1_0)−dcount1_2_0;

rangepings2=(count2_2−count2_0)−dcount2_2_0;

dist1_1=sqrt((x2_0+dx2_1)̂2+(y2_0+dy2_1)̂2);

dist1_2=sqrt((x2_0+dx2_1−dx1_2)̂2+(y2_0+dy2_1−dy1_2)̂2);

dist2_1=sqrt((x2_0−dx1_2)̂2+(y2_0−dy2_1)̂2);

dist2_2=sqrt((x2_0+dx2_2−dx1_2)̂2+(y2_0+dy2_2−dy2_1)̂2);

rangepings1=dist1_1+dist1_2;

rangepings2=dist2_1+dist2_2;  (6)

The last two lines in equations (6) a set of two equations and twounknowns, x2_0 and y2_0, representing the offset of car 2 at time 0. The‘count’ values in the first two lines are directly measured from the rawpseudo-range values, and all of the ‘d_XXX’ variables are provided bythe harmonic solutions family, thus being treated as constants.

The ‘asymptotic’ term in asymptotic solution families refers to refiningand connecting assumptions and approximations made by the methods andalgorithms. This is a central topic for the set physics material thatdeals with Planck-scale assemblies of PhaseNet nodes. In terrestrialapplications of PhaseNet, where the speed of communications issignificantly faster than the motion of the nodes, a one-step orpossibly a two-step iterative process is possible with strongconvergence properties. This convergence approach is used to correct forthe coarse metrics in the nominal solution families and the coarse rangeand coarse direction vectors used to generate harmonic solutions. Thisconvergence may barely justify use of the term ‘asymptotic,’ but aslarger groups of PhaseNet nodes create better harmonic solutions, thesebenefits also accrue to the final asymptotic solution families of largedefined groups and super-groups, with ever-refining space-time solutionsas more past data are used to place nodes within the asymptoticcollision frame. FIG. 62 is a quick sketch of one particular parameterin which the progression of solution types is a kind of asymptoticconvergence, with increasing time lags being the price paid for finerand finer solutions.

Space-Time Calibration Unit (SCU) Output

All three solution families are available to the client node, whichcontains the SCU. FIG. 62 thus represents the forms of output available,various time waveforms of position and clock drift (with X position ofcar A being the chosen example). Other parameters such as low bandwidthinstrumental delay are also possible as an internal parametric waveformwithin the harmonic solutions, as is orientation of the cars as anindependent variable (each car's compass direction of the front of thecar, for example). Interestingly, the ‘dcount’ family of waveforms is akind of correction to the coarse time representation in the horizontalaxis of FIG. 62, thus representing a given car/node's slight variancefrom the global set-wide definition of constant time. This has beendescribed in the set physics material and been dubbed ‘global tone 1’ orGT1, where a given node and small groups of nodes can actively solve fortheir minor deviations about the global average, adjusting their ratesas needed.

The previous disclosure described in detail the versatility of thedesign and application of systems implemented with the PhaseNetspace-time calibration technique. The following description presents aspecific implementation of the computations performed and solutionsgenerated to achieve overall system operation with error correction.

FIG. 63 is a graph showing clock counts corresponding to twotime-displaced ping events transmitted from a node A and received by anode B under conditions in which nodes A and B are the same distanceapart from each other during two ping events. With reference to FIG. 63,a vector PE1 represents a first ping event, which has a clock countvalue, Ping 1, expressed as

Ping 1=C _(r1B) −C _(t1A),  (7)

where C_(t1A) is a clock count (or count stamp) accumulated by a counterdriven by a digital clock residing at node A and transmitted by node Aat a time, T_(t1A), and C_(r1B) is a clock count (or count stamp)accumulated by a counter driven by a digital clock residing at Node Band associated with a time, T_(r1B), at which node B receives the firstping event transmitted by node A at T_(t1A). A vector PE2, represents asecond, later ping event, which has a clock count value, Ping 2,expressed as

Ping 2=C _(r2B) −C _(t2A),  (8)

where C_(t2A) is the clock count transmitted by node A at a time,T_(t2A), and C_(r2B) is the clock count associated with a time, T_(r2B),at which node B receives the second ping event transmitted by node A atT_(t2A). The straight line (ignoring incremental count quantization)plot of clock counts as a function of time for each of nodes A and Bindicate that their respective digital clocks, CLK_(A) and CLK_(B),operate at the same or a “system nominal” rate.

A differential clock count value representing the difference betweenPing 2 and Ping 1, Δ Ping_(AB), can be expressed as

$\begin{matrix}\begin{matrix}{{\Delta \; {Ping}_{AB}} = {{{Ping}\; 2} - {{Ping}\; 1}}} \\{= {\left( {C_{r\; 2B} - C_{t\; 2A}} \right) - {\left( {C_{r\; 1B} - C_{t\; 1A}} \right).}}}\end{matrix} & (9)\end{matrix}$

The entity Δ Ping_(AB)=0 when nodes A and B are the same distance apartfrom (i.e., not moving relative to) each other at the times of pingevents PE1 and PE2. This is the situation represented in FIG. 63, inwhich (T_(r1B)−T_(t1A)) and (T_(r2B)−T_(t2A)) are equal.

FIG. 64 is a graph showing clock counts corresponding to twotime-displaced ping events transmitted from node A and received by nodeB under conditions in which nodes A and B are different distances apartfrom each other during the two ping events. With reference to FIG. 64, avector PE1′ represents a first ping event having a Ping 1′ value that isthe same as the Ping 1 value of vector PE1. A vector PE2′ represents asecond, later ping event having a Ping 2′ value that is greater than thePing 2 value of vector PE2. A change in distance between nodes A and Bfor the first and second ping events is expressed as Δ Dist_(AB). Theinequalities Δ Ping_(AB)>0 and Δ Dist_(AB)>0 indicate that nodes A and Bmoved farther apart from each other between the times of the first andsecond ping events, as represented in FIG. 64. Similarly, theinequalities Δ Ping_(AB)<0 and Δ Dist_(AB)<0 indicate that nodes A and Bmoved closer to each other between the times of the first and secondping events.

FIG. 65 is a graph showing clock counts corresponding to twotime-displaced ping events transmitted from node A and received by nodeB under conditions in which nodes A and B are the same distance apartfrom each other during the two ping events but their clock rates aredissimilar. With reference to FIG. 65, (T_(r1B)−T_(t1A)) and(T_(r2B)−T_(t2A)) are equal; therefore, nodes A and B are not movingrelative to each other at the times of the first and second ping eventsPE1″ and PE2″. The clock count plots of nodes A and B indicate that theyare not parallel and that the node A clock, CLK_(A), counts at a slowerrate than the count rate of the node B clock, CLK_(B). FIG. 65 indicatesthat when the clock rate of CLK_(A) decreases relative to the systemnominal rate, Ping 2″ increases relative to Ping 2 of FIG. 63. Ingeneral, the following relationships characterize in ping counts changesin rate of node clock A, Δ CLK_(A), and node clock B, Δ CLK_(B):

-   -   Δ CLK_(A) decreases⇒Ping_(AB) increases    -   Δ CLK_(B) decreases⇒Ping_(AB) decreases.

The following two equations express, in terms of ping counts, changes inthe distance between nodes A and B, assuming that ping events are alsotransmitted from node B and received and count stamped by node A:

ΔPing_(AB) =K ₁ΔDist_(AB) −K ₂ ΔCLK _(A) +K ₃ ΔCLK _(B)  (10)

ΔPing_(BA) =K ₁ΔDist_(BA) +K ₂ ΔCLK _(B) −K ₃ ΔCLK _(A).  (11)

FIG. 66 is a diagram for use in illustrating the calculation of ΔDist_(AB). For small displacements during a unit ping interval (i.e.,during a short interval between successive pings), the term

ΔDist_(AB)=√{square root over (ΔX _(AB) ² +ΔY _(AB) ² +ΔZ _(AB)²)}  (12)

can be approximated. FIG. 66 is a diagram showing a straight line pathsegment between nodes A and B. With reference to FIG. 66, a straightline 20 connecting nodes A and B represents in x, y coordinate space thedisplacement of node B relative to node A for two successive pingevents. FIG. 66 shows that, for short time intervals between successiveping events and when node A remains stationary, the x and y componentsof Δ Dist_(AB) at node B can be expressed as Δ B_(x) cos θ and Δ B_(y)cos α, respectively, where Δ B_(x) and Δ B_(y) are the changes in therespective x and y coordinates of node B from its receipt of Ping 1 toits receipt of Ping 2, θ is the angle between line 20 and its projectiononto the x axis, and a is the angle between line 20 and its projectiononto the y axis. Similarly, in x, y, z coordinate space, the z componentof Δ Dist_(AB) can be expressed as Δ B_(z) cos φ.

When the three components are combined and the coordinates of node A areincluded, Δ Dist_(AB) can be expressed as

$\begin{matrix}{{\Delta \; {Dist}_{AB}} = {{\Delta \; B_{x}\cos \; \theta} + {\Delta \; B_{y}\cos \; \alpha} + {\Delta \; B_{z}\cos \; \phi} - {\left\lbrack {{\Delta \; A_{x}\cos \; \theta} + {\Delta \; A_{y}\cos \; \alpha} + {\Delta \; A_{z}\cos \; \phi}} \right\rbrack.}}} & (13)\end{matrix}$

Substituting into equation (10) the expression for Δ Dist_(AB) inequation (13) and taking into account the speed of light, c, for the E-Mimplementation provides

$\begin{matrix}{{\Delta \; {Ping}_{AB}} = {{\Delta \; {B_{x}\left( \frac{\cos \; \theta}{c} \right)}} + {\Delta \; {B_{y}\left( \frac{\cos \; \alpha}{c} \right)}} + {\Delta \; {B_{z}\left( \frac{\cos \; \phi}{c} \right)}} + {\Delta \; {A_{x}\left( \frac{{- \cos}\; \theta}{c} \right)}} + {\Delta \; {A_{y}\left( \frac{{- \cos}\; \alpha}{c} \right)}} + {\Delta \; {A_{z}\left( \frac{{- \cos}\; \phi}{c} \right)}} - {K_{2}\Delta \; {CLK}_{A}} + {K_{3}\Delta \; {{CLK}_{B}.}}}} & (14)\end{matrix}$

Simplifying equation (14) by relabelling the constant coefficients ofthe terms of Δ Dist_(AB),

ΔPing_(AB) =ΔA _(X) K _(AX) +ΔB _(X) K _(BX) +ΔA _(Y) K _(AY) +ΔB _(Y) K_(BY) +ΔA _(Z) K _(AZ) +ΔB _(Z) K _(BZ) −K ₂ ΔCLK _(A) +K ₃ ΔCLK_(B).  (15)

The solution of the Δ CLK_(A) and Δ CLK_(B) terms is developed withreference to FIG. 67. In FIG. 67, the horizontal line A represents atimeline of ping events transmitted by node A, and the horizontal line Brepresents a timeline of the ping events received by node B. Thevertical lines intersecting horizontal lines A and B are mutually spacedapart by a unit time interval, which represents the period of the systemnominal clock rate. The shorter-length tick marks on lines A and Bindicate the actual clock rates of CLK_(A) and CLK_(B), respectively. Avector PE1 represents a first ping event transmitted by a node A at atime A_(t1) established by CLK_(A) and received by node B at a timeB_(r1) established by CLK_(B). A vector PE2 represents a second pingevent transmitted by node A at a later time A_(t2) established byCLK_(A) and received by node B at a time B_(r2) established by CLK_(B).Transmit times A_(t1) and A_(t2) define respective time points P₁ andP₂, and receive times B_(r1) and B_(r2) define respective time points P₃and P₄. Inspection of FIG. 67 reveals that

P ₁ P ₂ + P ₂ P ₄ = P ₁ P ₃ + P ₃ P ₄ .  (16)

The term √{square root over (P₁P₂)} represents the time interval,measured in system nominal time, between the transmission of PE1 and thetransmission of PE2. Similarly, the term √{square root over (P₃P₄)}represents the system nominal time interval between the reception timesfor these ping events. The terms √{square root over (P₂P₄)} and √{squareroot over (P₁P₃)} represent the system nominal time intervals between,respectively, the transmission and the reception of PE2 and PE1. Morespecifically, with reference to FIG. 63,

√{square root over (P ₁ P ₂)}=(C _(t2A) −C _(t1A))−ΔCLK _(A12)  (17)

√{square root over (P ₃ P ₄)}=(C _(r2B) −C _(r1B))−ΔCLK _(B12),  (18)

where Δ CLK_(A12) and Δ CLK_(B12) represent the number of clock ticksneeded to correct to the system nominal clock rate for, respectively,CLK_(A) from the transmission time of first ping event PE1 to thetransmission time of second ping event PE2 and for CLK_(B) from thereceive time of PE1 to the receive time of PE2. Moreover, with referenceto FIG. 67,

$\begin{matrix}{\overset{\_}{P_{1}P_{3}} = {\frac{{Dist}_{1{ANB}}}{c}\mspace{14mu} {and}}} & (19) \\{{\overset{\_}{P_{2}P_{4}} = \frac{{Dist}_{2{AB}}}{c}},} & (20)\end{matrix}$

where Dist_(1AB) represents for the first ping event, PE1, the distancebetween nodes A and B from the transmit time recorded at node A to thereceive time recorded at node B, and Dist_(2AB) represents for thesecond ping event, PE2, the distance between nodes A and B from thetransmit time recorded at node A to the receive time recorded at node B.Thus, equation (12) also can be expressed as

$\begin{matrix}{\frac{\Delta \; {Dist}_{AB}}{c} = {\frac{{Dist}_{2{AB}}}{c} - {\frac{{Dist}_{1{AB}}}{c}.}}} & (21)\end{matrix}$

Substituting into equation (16) the right-hand side terms of equations(17), (18), (19), and (20) provides the following expression

$\begin{matrix}{{\left( {C_{t\; 2A} - C_{t\; 1A}} \right) - {\Delta \; {CLK}_{A\; 12}} + \frac{{Dist}_{2{AB}}}{c}} = {\frac{{Dist}_{1{AB}}}{c} + \left( {C_{r\; 2B} - C_{r\; 1B}} \right) - {\Delta \; {{CLK}_{B\; 12}.}}}} & (22)\end{matrix}$

Rearranging the terms of equation (16) provides

$\begin{matrix}{{{{- \Delta}\; {CLK}_{A\; 12}} + \frac{{Dist}_{2{AB}}}{c}} = {\frac{{Dist}_{1{AB}}}{c} + \left( {C_{r\; 2B} - C_{t\; 2A} - C_{r\; 1B} + C_{t\; 1A}} \right) - {\Delta \; {{CLK}_{B\; 12}.}}}} & (23)\end{matrix}$

Substituting into equation (23) the left-hand side terms of equations(9) and (21) results in the following expression

$\begin{matrix}{{{\Delta \; {Ping}_{AB}} = {\frac{\Delta \; {Dist}_{AB}}{c} + {\Delta \; {CLK}_{B\; 12}} - {\Delta \; {CLK}_{A\; 12}}}},} & (24)\end{matrix}$

where Δ CLK_(B12) and A CLK_(A12) represent corrections to,respectively, CLK_(B) and CLK_(A) to comport with the system nominalclock rate.

Equation (24) is in the form of the equation to which matrix algebra isapplied to solve for the unknown displacement values and changes inclock rates. The matrix equation is expressed as

g=Hf,  (25)

where g is a column vector of Δ Pings, the number of which is the numberof ping events minus 1; H is a two-dimensional matrix of coefficientsconstructed from the ping events; and f is a column vector of unknownsthat include changes in clock rate and location changes in x, y, and zdisplacements. The computation is carried out using harmonic blocks, inwhich there is a selected number of harmonic blocks for each equationand selected numbers of clock solutions and location solutions for eachharmonic block. The number of system nodes can change (above a certainminimum number of nodes), depending on whether certain nodes remain inthe system. Before equation (25) can be solved, all of the pinginformation is accumulated by at least one node in the network. Eachnode uses a pung broadcasting schedule to transmit to other nodes in thenetwork the ping information the node has received. By combining pingsand pungs that have been received, a node is able to reconstructinformation for all of the ping events of the network.

The table below presents qualitatively how the system of equations thatcan be developed becomes more overdetermined as nodes are added to anetwork. The first row of the table indicates that two nodes presenteight unknowns with two equations. The eight unknowns include, for eachnode, changes in clock time and changes in location in x, y, and zdisplacements. An example of the two equations is presented by equations(10) and (11). The table also reveals that, for an increasing number ofnodes, there is a widening difference between a larger number ofequations and a smaller number of unknowns. This difference indicatesthat a system network of a sufficient number of nodes can undergochanges to (by addition or subtraction of) the number of observed pingevents and still generate unique time and location solutions for thesystem nodes operating.

Number of Number of Number of Nodes Unknowns Equations 2 8 2 3 12 6 4 1612 5 20 20 6 24 30 7 28 42 8 32 56

The word “qualitatively” used in the previous paragraph connotes a needfor particular attention in the design of working systems around thegeneric networking principles expressed in the table set forthimmediately above. In particular, as but one of many examples, manysystems in which there are short communication baselines on the order ofa few kilometers or less begin to erode the independence of the duplexchannels. Likewise, nontrivial and highly variable instrumental delayerror sources also can become appreciable, explicit unknowns in H matrixformulations. Yet further subtleties in actual implementations entailthe use of “knowns” associated with one or both of the nodes and theenvironment, such as, for example, one node being immobile or a nodehappening to be slaved to an optical clock and treated as a masterclock. The primary important point of the table is to highlight thebenefit of the presence of more nodes in a network.

FIG. 68 is a diagram showing two exemplary harmonic block intervals,which are used to solve one matrix equation (25). For each harmonicblock, for example, there are one location solution and two clocksolutions generated for each node of, for example, a system of fiveoperating nodes. Ping events PE1 and PE2 are associated with a firstharmonic block, HB₁. Because there are specified two clock solutions foreach equation and one location for each equation, the length of columnvector f=50 (4 clock variables×5 nodes+2 locations×3 displacementvariables (x, y, z)×5 nodes).

With reference to FIG. 68, the two horizontal lines represent timelinesof CLK_(A) and CLK_(B), and the spaced-apart vertical lines define theboundaries for harmonic blocks HB₁ and HB₂. Harmonic blocks are formedbetween a priori regular intervals to generate solutions for clock andlocation variables relating to the transmission and receipt of pingevents at arbitrary times within the harmonic block intervals. Aharmonic block encompasses typically between five and ten ping events.The left-hand side vertical line of HB₁ is marked as location solution1, LS1, where one location solution and one clock solution are generatedfor each of CLK_(A) and CLK_(B). The right-hand side vertical line ofHB₁ is marked as location solution 2, LS2, where one location solutionand one clock solution are generated for each of CLK_(A) and CLK_(B). Asecond clock solution for each of CLK_(A) and CLK_(B) is generated at atime chosen a priori midway between LS1 and LS2. Because two clocksolutions for each node are computed in HB₁, the first and second clocksolutions are identified as CS₁ and CS₂, respectively, for each node.

A first ping event, PE1, and a second ping event, PE2, appear as shownin HB₁ and partly in HB₂. The transmit time difference between pingevents PE1 and PE2 is represented by (A_(t2)−A_(t1)), which is the timeinterval when Δ CLK_(Al2) occurs. FIG. 68 shows that the transmit timeinterval between A_(t1) and A_(t2) has a transmit midpoint, T_(M), andharmonic block HB₁ spans 15 time units. The clock solution CS₂ for nodeA takes place 3 time units after T_(M), and the clock solution CS₁ fornode A takes place 12 time units before T_(M). Interpolation weightingbetween successive time solutions provides, therefore, a − 3/15weighting for time solution t₁₁ and a − 12/15 weighting for timesolution t₁₂, where t₁₁ and t₁₂ are time solution matrix elements inwhich the left subscript represents the node and the right subscriptrepresents the solution index within the equation. (The minus signindicates transmit time counts.) The receive time difference betweenping events PE1 and PE2 is represented by (B_(r2)−B_(r1)), which is thetime interval when Δ CLK_(B12) occurs. FIG. 68 shows that the receivetime interval between B_(r1) and B_(r2) has a receive midpoint, R_(M).The clock solution CS₂ for node B takes place 7 time units before R_(M),and the clock solution CS₃ for node B takes place 8 time units afterR_(M). Interpolation weighting between successive time solutionsprovides, therefore, an 8/15 weighting for time solution t₂₂ and a 7/15weighting for time solution t₂₃. The time weighting is zero for t₂₁because clock solution CS2 lies between R_(M) and CS1.

The location solution LS1 for each of nodes A and B entails locationinterpolation weighting between successive location solutions, using theT_(M) and R_(M) interpolation benchmarks described for the timeinterpolation weighting. FIG. 68 shows that, for node A, LS1 lies 6 timeunits before T_(M) and LS2 lies 9 time units after T_(M). Interpolationweighting between successive location solutions provides, therefore, a −9/15 weighting for each of location solutions hx₁₁, hy₁₁, and hz₁₁ and a− 6/15 weighting for each of location solutions hx₁₂, hy₁₂, and hz₁₂.FIG. 68 shows that, for node B, LS1 lies 11 time units before R_(M) andLS2 lies 4 time units after R_(M). Interpolation weighting betweensuccessive location solutions provides, therefore, a 4/15 weighting foreach of location solutions hx₂₁, hy₂₁, and hz₂₁ and an 11/15 weightingfor each of location solutions hx₂₂, hy₂₂, and hz₂₂.

With reference to equation (14), the following are location coefficientvalues of LS1:

${hx}_{11} = {{{- 9}/15}\left( \frac{\cos \; \theta}{c} \right)}$${hy}_{11} = {{{- 9}/15}\left( \frac{\cos \; \alpha}{c} \right)}$${hz}_{11} = {{{- 9}/15}\left( \frac{\cos \; \phi}{c} \right)}$${hx}_{21} = {{4/15}\left( \frac{\cos \; \theta}{c} \right)}$${hy}_{21} = {{4/15}\left( \frac{\cos \; \alpha}{c} \right)}$${hz}_{21} = {{4/15}\left( \frac{\cos \; \phi}{c} \right)}$

and the following are location coefficient values of LS2:

${hx}_{12} = {{{- 6}/15}\left( \frac{\cos \; \theta}{c} \right)}$${hy}_{12} = {{{- 6}/15}\left( \frac{\cos \; \alpha}{c} \right)}$${hz}_{12} = {{{- 6}/15}\left( \frac{\cos \; \phi}{c} \right)}$${hx}_{22} = {{11/15}\left( \frac{\cos \; \theta}{c} \right)}$${hy}_{22} = {{11/15}\left( \frac{\cos \; \alpha}{c} \right)}$${hz}_{22} = {{11/15}{\left( \frac{\cos \; \phi}{c} \right).}}$

For the five nodes, location solution 1 and clock solution 1 arearranged as

-   -   x₁₁, y₁₁, z₁₁, t₁₁, X₂₁, y₂₁, z₂₁, t₂₁, . . . , x₅₁, y₅₁, z⁵¹,        t₅₁ to form the first 20 entries of the f column vector, and the        time solution is arranged as    -   t₁₂, t₂₂, t₃₂, t₄₂, t₅₂ to form 5 time entries concatenated to        the first 20 entries of the f column vector.

Similarly, for the five nodes, location solution 2 and clock solution 3are arranged as

-   -   x₁₂, y₁₂, z₁₂, t₁₃, x₂₂, y₂₂, z₂₂, t₂₃, . . . , x₅₂, y₅₂, z₅₂,        t₅₃,        and the time solution 4 is arranged as    -   t₁₄, t₂₄, t₃₄, t₄₄, t₅₄ to form 25 entries of the f column        vector.

The values of θ, α, and φ are realizable based upon coordinates assignedto nodes A and B in the nominal solution. The remaining locationcoefficient values of LS1 and LS2 for the remaining solution indices andfor successive harmonic blocks are determined in analogous manner.

In this embodiment, the system clock is defined as the average of thenode clocks. Skilled persons will appreciate that many other systemclock determination techniques are possible. For example, if one of thesystem nodes operates with an atomic clock, that clock can establish thesystem clock with extremely small clock drift. Also, in this embodiment,the first four nodes are specified as fixed fiducial nodes. They areplaced in fixed and known locations and can provide a reference for thesolution of other node(s). Additional equations are added to the systemof equations described above to provide this clock and locationdetermination. The equations used in this embodiment to aid in clockdetermination are:

0=t ₁₁ +t ₂₁ +t ₃₁ +t ₄₁ +t ₅₁

0=t ₁₂ +t ₂₂ +t ₃₂ +t ₄₂ +t ₅₂

0=t ₁₃ +t ₂₃ +t ₃₃ +t ₄₃ +t ₅₃

0=t ₁₄ +t ₂₄ +t ₃₄ +t ₄₄ +t ₅₄.

The scalar equations used in this embodiment to aid in locationdetermination are:

0=x ₁₁

0=y ₁₁

0=z ₁₁

0=x ₂₁

0=y ₂₁

0=z ₂₁

0=x ₃₁

0=y ₃₁

0=z ₃₁

0=x ₄₁

0=y ₄₁

0=z ₄₁

0=x ₁₂

0=y ₁₂

0=z ₁₂

0=x ₂₂

0=y ₂₂

0=z ₂₂

0=x ₃₂

0=y ₃₂

0=z ₃₂

0=x ₄₂

0=y ₄₂

0=z ₄₂

In the example of solution vector calculation given with reference toFIGS. 63-68, the iterative location and time solutions generated fornodes A and B in harmonic block intervals define solution path segments.Each solution path segment is generated without reference to previoussolution path segments; therefore, without correction, solution pathsegment errors accumulate in the concatenation of successive pathsegments. FIG. 69 is a diagram showing in solid lines for node B anexample of a solution path formed by concatenation of three solutionpath segments, each having a start location (indicated by “x”) and afinish location (indicated by “o”). FIG. 69 also shows in dashed linesthe corresponding actual path traveled by node B. The objective of areferenced solution, which in this example is an asymptotic solution, isto form a corrected solution path by positioning the start location of asecond (i.e., next succeeding) location solution at the optionallocation, without regard to the actual finish location of a first (i.e.,immediately preceding the second) location solution.

The following analysis presents a derivation of a system equation, inwhich on one side of the equal sign there is one scalar observable (oralready solved) quantity and on the other side of the equal sign thereis a linear combination of starting times for, and locations at thestart of, the transmitting and receiving nodes. The start is defined asthe beginning of a current solution iteration.

The following is a set of definitions of terms:t(−) is system time at event−ping_tx=a ping transmit eventping_rx=a ping receive eventraw_ping_dist=actual (system) distance of ping travelrx_time(−) is receive node's (noisy) proper clock at event−tx_time(−) is transmit node's (noisy) proper clock at event−tx_iter_blk_start=start of transmit iteration block eventrx_iter_blk_start=start of receive iteration block eventThe analysis begins with equations (26) and (27):

t(ping_rx)−t(ping_tx)=raw_ping_dist/speed_of_light  (26)

raw_ping_time=rx_time(ping_rx)−tx_time(ping_tx).  (27)

Adding and subtracting equation (26) on the right-hand side of equation(27):

raw_ping_time=raw_ping_dist/speed_oflight−t(ping_rx)+t(ping_tx)+rx_time(ping_rx)−tx_time(ping_tx)  (28)

raw_ping_time=raw_ping_dist/speed_oflight+[rx_time(ping_rx)−t(ping_rx)]−[tx_time(ping_tx)−t(ping_tx)].  (29)

Recasting to terms of start of a solved iteration blk:

tx_start=t(tx_iter_blk_start)=desired value for asymptoticsolution  (30a)

rx_start=t(rx_iter_blk_start)=desired value for asymptoticsolution.  (30b)

The tx harmonic time solution at ping_tx is:

tx_harmonic_clk(ping_tx)=[tx_time(ping_tx)−t(ping_tx)]−[tx_time(tx_iter_blk_start)−t(tx_iter_blk_start)].  (31a)

The rx harmonic time solution at ping_rx is:

rx_harmonic_clk(ping_rx)=[rx_time(ping_rx)−t(ping_rx)]−[rx_time(rx_iter_blk_start)−t(rx_iter_blk_start)].  (31b)

Adding equation (31a) and −1* equation (31b) to equation (29):

raw_ping_time+tx_harmonic_clk(ping_tx)−rx_harmonic_clk(ping_rx)=raw_ping_dist/speed_oflight−[tx_time(tx_iter_blk_start)−t(tx_iter_blk_start)]+[rx_time(rx_iter_blk_start)−t(rx_iter_blk_start)].  (32)

Placing the ping distance back in terms of start of solved iterationblk;

nominal_dist=∥nominal_tx(tx_iter_blk_start)−nominal_rx(rx_iter_blk_start)∥  (33)

raw_ping_dist=nominal_dist−tx_delta_start*dir_cos(tx_rx)+rx_delta_start*dir_cos(tx_rx)−[tx_harmonic_loc(ping_tx)*dir_cos(tx_rx)++rx_harmonic_loc(ping_rx)*dir_cos(tx_rx)].  (34)

In equation (34), the terms “tx_delta_start” and “tr_delta_start” referto the quantities δx, δy, and δz for node A (transmit node) and node B(receive node) shown in FIG. 70, referenced below. The term“dir_cos(tx_rx)” refers to the ping event vector components in x, y, zcoordinate space, as discussed above with reference to FIG. 66.Substituting equation (34) into equation (32), and placing observablesand pre-existing solutions on left-hand side:

raw_ping_time+tx_harmonic_clk(ping_tx)−rx_harmonic_clk(ping_rx)+[tx_time(tx_iter_blk_start)−rx_time(rx_iter_blk_start)]+[tx_harmonic_loc(ping_tx)*dir_cos(txc_rx)−rx_harmonic_loc(ping_rx)*dir_cos(tx_rx)−nominal_dist]/speed_oflight=t(tx_iter_blk_start)−t(rx_iter_blkstart)+[rx_delta_start*dir_cos(tx_rx)−tx_delta_start*dir_cos(rx_tx]/speed_oflight.  (35)

Solving now for delta time, rather than absolute time:

t(tx_iter_blk_start)=tx_time(tx_iter_blk_start)+tx_delta_start_clk

t(rx_iter_blk_start)=rx_time(rx_iter_blk_start)+rx_delta_start_clk.  (36)

The time-related terms “tx_delta_start_clk” and “rx_delta_start_clk” arethe unknown quantities for which solutions are generated.

The following matrix equation corresponds to equation (18).

$\begin{bmatrix}{{Ping}\; 1} \\{{Ping}\; 2} \\{{Ping}\; 3} \\{{Ping}\; 4} \\{{Ping}\; 5} \\\vdots\end{bmatrix} = {\lbrack A\rbrack \begin{bmatrix}\delta_{x\; 1} \\\delta_{y\; 1} \\\delta_{z\; 1} \\\delta_{t\; 1} \\\delta_{x\; 2} \\\vdots\end{bmatrix}}$

The left-hand side is a column vector of ping events, the right-side isa product of a column vector of the delta location (x, y, z) and deltatime solution variables and a two-dimensional “A” matrix of coefficientsalready known. For each node, the solutions for δx, δy, and δz are startposition offsets and the solution for δt is a start time offset from,respectively, the finish location and the finish time of the previoussolution interval. FIG. 70 is a diagram showing an example in x, ycoordinate space, as a result of the solutions generated from equation(37), the repositioning of new asymptotic solutions to the finishlocations of the previous solutions for node A and node B.

raw_ping_time+tx_harmonic_clk(ping_tx)−rx_harmonic_clk(ping_rx)+[tx_harmonic_loc(ping_tx)*dir_cos(tx_rx)−rx_harmonic_loc(ping_rx)*dir_cos(tx_rx)−nominal_dist]/speed_oflight=tx_delta_start_clk−rx_delta_start_clk+[rx_delta_start*dir_cos(tx_rx)−tx_delta_start*dir_cos(rx_tx)]/speed_oflight.  (37)

Equation (37) above is the equation sought, and the objective is to finda value for the left-hand side. The coefficients on the right arealready known.

In this embodiment, equations are added to the system of equationsdescribed above. The following equation is added to provide clockcorrection solutions:

0=δ_(t1)+δ_(t2)+δ_(t3)αδ_(t4)+δ_(t5).

The following equations present the unknowns δ_(x1), δb_(y1), and δ_(z1)that represent the start position for the location solution of node 1.

Node₁(x)=δ_(x1)

Node₁(y)=δ_(y1)

Node₁(z)=δ_(z1).

There are similar equations added for the other four nodes of thenetwork.

It will be obvious to those having skill in the art that many changesmay be made to the details of the above-described embodiments withoutdeparting from the underlying principles of the invention. The scope ofthe present invention should, therefore, be determined only by thefollowing claims.

1. A space-time calibration method of determining position and timinginformation relating to one or more nodes of multiple nodes occupyinglocations in a spatial region and communicating with one another,comprising: establishing wireless communication among multiple nodes ina spatial region, including transmitting nodes and receiving nodes, thetransmitting nodes transmitting ping events and the receiving nodesreceiving and associating receive count stamps to the transmitted pingevents, the receive count stamps based on independent clocks operatingin the receiving nodes and characterized by clock rate differences;developing pung signals representing a record of the ping eventsreceived by the receiving nodes during a data recording time interval,the record for each receiving node including the receive count stampscorresponding to the ping events received during the data recording timeinterval; and processing the pung signals to determine, for a movingnode of the multiple nodes, location information corresponding to alocation occupied by the moving node in the spatial region and a clockrate difference characterizing an independent clock operating in thenode relative to an independent clock in another node.
 2. The method ofclaim 1, further comprising associating with transmit count stamps theping events transmitted by the transmitting nodes, the transmit countstamps based on independent clocks operating in the transmitting nodesand characterized by clock rate differences, and in which thetransmitted ping events received by the receiving nodes include transmitcount-stamped ping events.
 3. The method of claim 1, in which the numberof nodes includes a set of transmit nodes, and in which the processingof the ping signals determines from the location information a spatialdistribution of member nodes of the set of transmit nodes in the spatialregion.
 4. The method of claim 1, in which the number of nodes includesa set of receive nodes, and in which the processing of the ping signalsdetermines from the location information a spatial distribution ofmember nodes of the set of receive nodes in the spatial region.
 5. Themethod of claim 1, in which the number of nodes includes a set of duplexnodes, and in which the processing of the ping signals determines fromthe location information a spatial distribution of member nodes of theset of duplex nodes in the spatial region.
 6. The method of claim 1, inwhich the record of the ping events includes differential count stampvalues corresponding to differences between the receive count stamps andthe associated transmit count stamps.
 7. The method of claim 1, in whichthe association of the transmit count stamps entails applying thetransmit count stamps to the ping events transmitted.
 8. The method ofclaim 1, in which the association of the receive count stamp entailsapplying the receive count stamps to the ping events received.
 9. Themethod of claim 1, further comprising deriving a system clock from theclock rate differences of the multiple nodes.
 10. The method of claim 1,in which at least one of the number of multiple nodes changes locationwith time to describe a path of motion characterized by concatenatedpath segments having start and finish locations, and the processing ofpung signals further comprises determining from a previous one of thepath segments a corrected start location of and a corrected clock for acurrent one of the path segments to reduce cumulative error in thedetermination of the location occupied by the node and the clockinstability function of the clock operating in the node.
 11. The methodof claim 10, in which the processing of the pung signals generates areferenced solution for the corrected start location and correctedclock.
 12. A space-time calibration engine, comprising: an inputconfigured to receive count-stamped information signals propagatingthrough a medium from multiple nodes occupying a spatial region, themultiple nodes including source and receiving nodes operating withindependent clocks characterized by clock rate differences, and thecount-stamped information signals carrying information relating totransmit and receive events of the source and receiving nodes; and aprocessor determining from information carried by the count-stampedinformation signals, for a moving node of the multiple nodes, locationinformation corresponding to a location occupied by the moving node inthe spatial region and a clock rate difference characterizing the clockoperating in the node relative to an independent clock in another node.13. The space-time calibration engine of claim 12, in which at least oneof the count-stamped information signals propagating from the multiplenodes is generated remotely from the node.
 14. The space-timecalibration engine of claim 12, in which at least one of thecount-stamped information signals propagating from the multiple nodes isgenerated within the node.
 15. The space-time calibration engine ofclaim 12, in which at least one of the number of multiple nodes changeslocation with time to describe a path of motion characterized byconcatenated path segments having start and finish locations, and inwhich the processor determines from a previous one of the path segmentsa corrected start location of and a corrected clock for a current one ofthe path segments to reduce cumulative error in the determination of thelocation occupied by the node and clock rate difference of the clockoperating in the node.
 16. The space-time calibration engine of claim15, in which the processor generates a referenced solution for thecorrected start location and corrected clock.
 17. The space-timecalibration engine of claim 12, in which the input and the processor areimplemented at a remote location from the locations of the multiplenodes.
 18. The space-time calibration engine of claim 12, furthercomprising multiple ones of the input and multiple ones of the processorrecited in claim 11, and in which each of at least some of the multiplenodes is implemented with one of the inputs and one of the processors.19. The space-time calibration engine of claim 12, in which theprocessor determines the location information and clock rate differencesby generating, over solution intervals, solutions indicating changes inlocation occupied by the node and changes in clock rate characterizingthe clock operating in the node.